Relevant bibliographies by topics / Principal components analysis (pca)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Principal components analysis (pca)'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Principal components analysis (pca)"

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Maćkiewicz, Andrzej, and Waldemar Ratajczak. "Principal components analysis (PCA)." Computers & Geosciences 19, no. 3 (March 1993): 303–42. http://dx.doi.org/10.1016/0098-3004(93)90090-r.

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Abegaz, Fentaw, Kridsadakorn Chaichoompu, Emmanuelle Génin, David W. Fardo, Inke R. König, Jestinah M. Mahachie John, and Kristel Van Steen. "Principals about principal components in statistical genetics." Briefings in Bioinformatics 20, no. 6 (September 14, 2018): 2200–2216. http://dx.doi.org/10.1093/bib/bby081.

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Abstract Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.
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Jensen, Matt, Trent Stellingwerff, Courtney Pollock, James Wakeling, and Marc Klimstra. "Can Principal Component Analysis Be Used to Explore the Relationship of Rowing Kinematics and Force Production in Elite Rowers during a Step Test? A Pilot Study." Machine Learning and Knowledge Extraction 5, no. 1 (February 17, 2023): 237–51. http://dx.doi.org/10.3390/make5010015.

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Investigating the relationship between the movement patterns of multiple limb segments during the rowing stroke on the resulting force production in elite rowers can provide foundational insight into optimal technique. It can also highlight potential mechanisms of injury and performance improvement. The purpose of this study was to conduct a kinematic analysis of the rowing stroke together with force production during a step test in elite national-team heavyweight men to evaluate the fundamental patterns that contribute to expert performance. Twelve elite heavyweight male rowers performed a step test on a row-perfect sliding ergometer [5 × 1 min with 1 min rest at set stroke rates (20, 24, 28, 32, 36)]. Joint angle displacement and velocity of the hip, knee and elbow were measured with electrogoniometers, and force was measured with a tension/compression force transducer in line with the handle. To explore interactions between kinematic patterns and stroke performance variables, joint angular velocities of the hip, knee and elbow were entered into principal component analysis (PCA) and separate ANCOVAs were run for each performance variable (peak force, impulse, split time) with dependent variables, and the kinematic loading scores (Kpc,ls) as covariates with athlete/stroke rate as fixed factors. The results suggested that rowers’ kinematic patterns respond differently across varying stroke rates. The first seven PCs accounted for 79.5% (PC1 [26.4%], PC2 [14.6%], PC3 [11.3%], PC4 [8.4%], PC5 [7.5%], PC6 [6.5%], PC7 [4.8%]) of the variances in the signal. The PCs contributing significantly (p ≤ 0.05) to performance metrics based on PC loading scores from an ANCOVA were (PC1, PC2, PC6) for split time, (PC3, PC4, PC5, PC6) for impulse, and (PC1, PC6, PC7) for peak force. The significant PCs for each performance measure were used to reconstruct the kinematic patterns for split time, impulse and peak force separately. Overall, PCA was able to differentiate between rowers and stroke rates, and revealed features of the rowing-stroke technique correlated with measures of performance that may highlight meaningful technique-optimization strategies. PCA could be used to provide insight into differences in kinematic strategies that could result in suboptimal performance, potential asymmetries or to determine how well a desired technique change has been accomplished by group and/or individual athletes.
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López-Rubio, Ezequiel, Juan Miguel Ortiz-de-Lazcano-Lobato, José Muñoz-Pérez, and José Antonio Gómez-Ruiz. "Principal Components Analysis Competitive Learning." Neural Computation 16, no. 11 (November 1, 2004): 2459–81. http://dx.doi.org/10.1162/0899766041941880.

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We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.
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Bijarania, Subhash, Anil Pandey, Mainak Barman, Monika Shahani, and Gharsi Ram. "Assesment of divergence among soybean [Glycine max (L.) Merrill] genotypes based on phenological and physiological traits." Environment Conservation Journal 23, no. 1&2 (February 11, 2022): 72–82. http://dx.doi.org/10.36953/ecj.021808-2117.

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A study was conducted to understand genetic divergence in Randomized complete block design accommodating 30 soybean [Glycine max (L.) Merrill] genotypes randomly in three replications. These genotypes were evaluated for twenty-seven traits: five phenological, nine agro-morphological, eight physiological traits (from field-trial) and five physiological traits (from laboratory experiment) recorded and subjected to PCA (Principal Component Analysis) and cluster analysis. Among all the studied cultivars, significant diversity, as well as analysis of dispersion, was recorded for different agro-morphological characters. D2-statistic (Tocher method) framed (generalized distance-based) nine clusters: largest with eight and five were oligo-genotypic. Harvest index>seed yield per plant>germination relative index>seedling dry weight contributed maximum towards total divergence. From the most divergent clusters, 21 crosses involving cluster v genotypes (PS-1347, RKS-18, PS-1092, NRC-142, VLS-94, NRC-136, and Shalimar Soybean-1) with monogenotypic cluster VII (AMS-2014), VIII (RSC-11-15) and III (RSC-10-71) suggested for future hybridization. Out of eighteen, only eight principal components revealed more than 1.00 eigen value and exhibited about 85.03% variability among the traits studied. The highest variability (25.41%) by PC1 followed by PC2 (15.60%), PC3 (12.35%), PC4 (10.13%), PC5 (7.20%), PC6 (5.43%), PC7 (4.80%) and PC8 (4.11%) for characters under study.
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Sonaniya, Rahul, Rajani Bisen, and Pallavi Sonaniya. "Assessment of Exotic Sesame (Sesamum indicum) Accessions through Principal Component Analysis." International Journal of Environment and Climate Change 13, no. 11 (October 7, 2023): 282–90. http://dx.doi.org/10.9734/ijecc/2023/v13i113170.

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The present research conducted over 96 exotic sesame accessions with five checks, to identify the minimum number of components, which could explain maximum variability out of the total variability using Principal Component Analysis (PCA); The investigation was performed under Project Co-ordinating Unit (Sesame and Niger) Research Farm, JNKVV, Jabalpur (M.P.) during kharif 2018 using Augmented block design. Among the studied traits, Component 1 had the contribution from the traits viz., number of primary branches per plant, number of capsules per plant, number of seeds per capsule, oil content and seed yield/plant, which accounted 30.71% to the total variability. Days to flower initiation and days to 50% flowering had contributed 17.11% to the total variability in component 2. The remaining variabilities of 11.26%, 9.94%, 7.48% and 6.73% were consolidated in PC3, PC4, PC5 and PC6 respectievely by various traits like number of secondary branches/plant, capsule length, days to maturity, thousand seed weight and plant height . The cumulative variance of 83.23% of total variation among 12 characters was explained by the first six axes. On the basis of PC scores PC1, PC3 and PC5 accounting mainly to yield and quality traits containing accesions viz., EC-334998, ES-38, EC-346426, EC-334958, EC-340538, RT-351 and GT-10 might be further utilized in breeding programme.
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Kondi, Ravi, Sonali Kar, and Soumya Surakanti. "Agro-morphological and biochemical characterization and principal component analysis for yield and quality characters in fine-scented rice genotypes." Genetika 54, no. 3 (2022): 1005–21. http://dx.doi.org/10.2298/gensr2203005k.

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Forty-one fine-scented rice genotypes were evaluated for 18 agro-morphological and quality characters for characterization, and 21 quantitative characters were evaluated for principal component analysis in R-studio software. Characterization of agro-morphological traits viz., plant height, days to 50% flowering, panicle length, number of effective tillers per plant, test weight, grain length, grain breadth, grain L: B ratio, kernel length, kernel breadth, kernel dimensions, awns, colour of awns, distribution of awns, and quality traits viz., alkali spreading value, gel consistency, grain aroma, and amylose content showed huge diversity among the genotypes. PCA revealed that PC1 showed the highest amount of variance (32.0%) followed by PC2 (15.7%), PC3 (9.0%), PC4 (8.1%), PC5 (7.8%), PC6 (5.4%) for quantitative characters. Out of 21 principal components, only 6 showed an eigenvalue greater than 1 and contributes about 78.1% total variance Genotypes in PC1 showed higher values for grain L: B ratio and kernel L: B ratio. Similarly, PC2 showed higher variable values for characters like test weight, kernel length, grain length, grain breadth, alkali spreading value, grain yield per plot and amylose content. PC3 for harvest index, panicle length, gel consistency, no. of effective tillers per plant and head rice recovery. PC4 for characters like plant height, kernel breadth and days to 50% flowering. PC5 for characters like kernel elongation ratio, and filled grains per panicle. PC6 for characters like no. of tillers in a square meter and no. of panicles in a square meter. This pre-breeding characterization study may be useful in finding potential genotypes which are having both yield and quality characters which may be useful in breeding for high-yielding varieties with good-quality characters.
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Tiwari, Priya, and Stuti Sharma. "Principal component analyses in mungbean genotypes under summer season." INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 17, no. 2 (June 15, 2021): 287–92. http://dx.doi.org/10.15740/has/ijas/17.2/287-292.

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Yield is a complex trait subjective to several components and environmental factors. Therefore, it becomes necessary to apply such technique which can identify and prioritize the key traits to lessen the number of traits for valuable selection and genetic gain. Principal component analysis is primarily a renowned data reduction technique which identifies the least number of components and explain maximum variability, it also rank genotypes on the basis of PC scores. PCA was calculated using Ingebriston and Lyon (1985) method. In present study, PCA performed for phenological and yield component traits presented that out of thirteen, only five principal components (PCs) exhibited more than 1.00 eigen value, and showed about 80.28 per cent of total variability among the traits. Scree plot explained the percentage of variance associated with each principal component obtained by illustrating a graph between eigen values and principal component numbers. PC1 showed 26.12 per cent variability with eigen value 3.40. Graph depicted that the maximum variation was observed in PC1 in contrast to other four PCs. The PC1 was further associated with the phenological and yield attributing traits viz., number of nodes per plant, number of pod cluster per plant, number of pod per plant. PC2 exhibited positive effect for harvest index. The PC3 was more related to yield related traits i.e., number of seeds per pod, number of seeds per plant and biological yield per plant, whereas PC4 was more loaded with phenological traits. PC5 was further related to yield and yield contributing traits i.e. number of primary branches per plant, seed yield per plant and 100 seed weight. A high value of PC score of a particular genotype in a particular PC denotes high value for those variables falling under that specific principal component. Pusa Vishal found in PC 2, in PC 3, PC 4 and PC 5, can be considered as an ideal breeding material for selection and for further deployment in defined breeding programme.
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Uikey, Shivani, Stuti Sharma, M. K. Shrivastava, and Pawan K. Amrate. "Study of principal component analyses for pod traits in soybean." INTERNATIONAL JOURNAL OF AGRICULTURAL SCIENCES 17, no. 2 (June 15, 2021): 341–49. http://dx.doi.org/10.15740/has/ijas/17.2/341-349.

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Yield being a complex entity influenced by several components and environments. PCA is a well-known method of dimension reduction that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set (Massay, 1965 and Jolliffie, 1986). In present study, PCA preformed for pod and yield traits revealed that out of fourteen, only five principal components (PCs) exhibited more than 1.0 eigen value and showed about 70.44% total variability among the traits. PC1 showed 23.83% variability with eigen value 3.33 indicating the maximum variation in comparison to other four PCs. The PC1 was more related to traits viz., days to 50% flowering, total number of pods per plant, number of seeds per plant, biological yield per plant and seed yield per plant. Thus, PC1 allowed for simultaneous selection of yield related traits and it can be regarded as yield factor. PC2 exhibited positive effect for days to maturity, number of primary branches per plant and number of nodes per plant, The PC3 was more related to number of two seeded pods per plant, 100 seed weight and harvest index traits, whereas PC4 was more loaded with three seeded pods per plant. PC5 was more related to plant height and number of one seeded pods per plant. A high value of PC score of a particular advanced line in a particular PC denotes high value for those variables. Genotypes namely KS 103, JS 20-15, PS 1423, Cat 1957, Cat 1958, JS 20-06 and JS 20-66 can be considered an ideotype breeding material for selection and for further utilization in precise breeding programme.
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Gewers, Felipe L., Gustavo R. Ferreira, Henrique F. De Arruda, Filipi N. Silva, Cesar H. Comin, Diego R. Amancio, and Luciano Da F. Costa. "Principal Component Analysis." ACM Computing Surveys 54, no. 4 (May 2021): 1–34. http://dx.doi.org/10.1145/3447755.

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Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying PCA, data standardization, possible visualizations of the PCA results, and outlier detection are subsequently addressed. Next, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets. Finally, we summarize PCA-related approaches and other dimensionality reduction techniques. All in all, the objective of this work is to assist researchers from the most diverse areas in using and interpreting PCA.
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Dissertations / Theses on the topic "Principal components analysis (pca)"

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Le, Hanh T. Banking &amp Finance Australian School of Business UNSW. "Discrete PCA: an application to corporate governance research." Awarded by:University of New South Wales. Banking & Finance, 2007. http://handle.unsw.edu.au/1959.4/40753.

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This thesis introduces the application of discrete Principal Component Analysis (PCA) to corporate governance research. Given the presence of many discrete variables in typical governance studies, I argue that this method is superior to standard PCA that has been employed by others working in the area. Using a dataset of 244 companies listed on the London Stock Exchange in the year 2002-2003, I find that Pearson's correlations underestimate the strength of association between two variables, when at least one of them is discrete. Accordingly, standard PCA performed on the Pearson correlation matrix results in biased estimates. Applying discrete PCA on the polychoric correlation matrix, I extract from 28 corporate governance variables 10 significant factors. These factors represent 8 main aspects of the governance system, namely auditor reputation, large shareholder influence, size of board committees, social responsibility, risk optimisation, director independence level, female representation and institutional ownership. Finally, I investigate the relationship between corporate governance and a firm's long-run share market performance, with the former being the factors extracted. Consistent with Demsetz' (1983) argument, I document limited explanatory power for these governance factors.
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Allemang, Matthew R. "Comparison of Automotive Structures Using Transmissibility Functions and Principal Component Analysis." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367944783.

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Massaro, James. "A PCA based method for image and video pose sequencing /." Online version of thesis, 2010. http://hdl.handle.net/1850/11991.

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Solat, Karo. "Generalized Principal Component Analysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.

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The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA). The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals.
Ph. D.
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Ragozzine, Brett A. "Modeling the Point Spread Function Using Principal Component Analysis." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1224684806.

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Renkjumnong, Wasuta. "SVD and PCA in Image Processing." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/31.

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The Singular Value Decomposition is one of the most useful matrix factorizations in applied linear algebra, the Principal Component Analysis has been called one of the most valuable results of applied linear algebra. How and why principal component analysis is intimately related to the technique of singular value decomposition is shown. Their properties and applications are described. Assumptions behind this techniques as well as possible extensions to overcome these limitations are considered. This understanding leads to the real world applications, in particular, image processing of neurons. Noise reduction, and edge detection of neuron images are investigated.
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Li, Liubo Li. "Trend-Filtered Projection for Principal Component Analysis." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277234178696.

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Nelson, Philip R. C. MacGregor John F. Taylor Paul A. "The treatment of missing measurements in PCA and PLS models /." *McMaster only, 2002.

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Bianchi, Marcelo Franceschi de. "Extração de características de imagens de faces humanas através de wavelets, PCA e IMPCA." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-10072006-002119/.

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Reconhecimento de padrões em imagens é uma área de grande interesse no mundo científico. Os chamados métodos de extração de características, possuem as habilidades de extrair características das imagens e também de reduzir a dimensionalidade dos dados gerando assim o chamado vetor de características. Considerando uma imagem de consulta, o foco de um sistema de reconhecimento de imagens de faces humanas é pesquisar em um banco de imagens, a imagem mais similar à imagem de consulta, de acordo com um critério dado. Este trabalho de pesquisa foi direcionado para a geração de vetores de características para um sistema de reconhecimento de imagens, considerando bancos de imagens de faces humanas, para propiciar tal tipo de consulta. Um vetor de características é uma representação numérica de uma imagem ou parte dela, descrevendo seus detalhes mais representativos. O vetor de características é um vetor n-dimensional contendo esses valores. Essa nova representação da imagem propicia vantagens ao processo de reconhecimento de imagens, pela redução da dimensionalidade dos dados. Uma abordagem alternativa para caracterizar imagens para um sistema de reconhecimento de imagens de faces humanas é a transformação do domínio. A principal vantagem de uma transformação é a sua efetiva caracterização das propriedades locais da imagem. As wavelets diferenciam-se das tradicionais técnicas de Fourier pela forma de localizar a informação no plano tempo-freqüência; basicamente, têm a capacidade de mudar de uma resolução para outra, o que as fazem especialmente adequadas para análise, representando o sinal em diferentes bandas de freqüências, cada uma com resoluções distintas correspondentes a cada escala. As wavelets foram aplicadas com sucesso na compressão, melhoria, análise, classificação, caracterização e recuperação de imagens. Uma das áreas beneficiadas onde essas propriedades tem encontrado grande relevância é a área de visão computacional, através da representação e descrição de imagens. Este trabalho descreve uma abordagem para o reconhecimento de imagens de faces humanas com a extração de características baseado na decomposição multiresolução de wavelets utilizando os filtros de Haar, Daubechies, Biorthogonal, Reverse Biorthogonal, Symlet, e Coiflet. Foram testadas em conjunto as técnicas PCA (Principal Component Analysis) e IMPCA (Image Principal Component Analysis), sendo que os melhores resultados foram obtidos utilizando a wavelet Biorthogonal com a técnica IMPCA
Image pattern recognition is an interesting area in the scientific world. The features extraction method refers to the ability to extract features from images, reduce the dimensionality and generates the features vector. Given a query image, the goal of a features extraction system is to search the database and return the most similar to the query image according to a given criteria. Our research addresses the generation of features vectors of a recognition image system for human faces databases. A feature vector is a numeric representation of an image or part of it over its representative aspects. The feature vector is a n-dimensional vector organizing such values. This new image representation can be stored into a database and allow a fast image retrieval. An alternative for image characterization for a human face recognition system is the domain transform. The principal advantage of a transform is its effective characterization for their local image properties. In the past few years researches in applied mathematics and signal processing have developed practical wavelet methods for the multi scale representation and analysis of signals. These new tools differ from the traditional Fourier techniques by the way in which they localize the information in the time-frequency plane; in particular, they are capable of trading on type of resolution for the other, which makes them especially suitable for the analysis of non-stationary signals. The wavelet transform is a set basis function that represents signals in different frequency bands, each one with a resolution matching its scale. They have been successfully applied to image compression, enhancement, analysis, classification, characterization and retrieval. One privileged area of application where these properties have been found to be relevant is computer vision, especially human faces imaging. In this work we describe an approach to image recognition for human face databases focused on feature extraction based on multiresolution wavelets decomposition, taking advantage of Biorthogonal, Reverse Biorthogonal, Symlet, Coiflet, Daubechies and Haar. They were tried in joint the techniques together the PCA (Principal Component Analysis) and IMPCA (Image Principal Component Analysis)
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Anjasmara, Ira Mutiara. "Spatio-temporal analysis of GRACE gravity field variations using the principal component analysis." Thesis, Curtin University, 2008. http://hdl.handle.net/20.500.11937/957.

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Gravity Recovery and Climate Experiment (GRACE) mission has amplified the knowledge of both static and time-variable part of the Earth’s gravity field. Currently, GRACE maps the Earth’s gravity field with a near-global coverage and over a five year period, which makes it possible to apply statistical analysis techniques to the data. The objective of this study is to analyse the most dominant spatial and temporal variability of the Earth’s gravity field observed by GRACE using a combination of analytical and statistical methods such as Harmonic Analysis (HA) and Principal Component Analysis (PCA). The HA is used to gain general information of the variability whereas the PCA is used to find the most dominant spatial and temporal variability components without having to introduce any presetting. The latter is an important property that allows for the detection of anomalous or a-periodic behaviour that will be useful for the study of various geophysical processes such as the effect from earthquakes. The analyses are performed for the whole globe as well as for the regional areas of: Sumatra- Andaman, Australia, Africa, Antarctica, South America, Arctic, Greenland, South Asia, North America and Central Europe. On a global scale the most dominant temporal variation is an annual signal followed by a linear trend. Similar results mostly associated to changing land hydrology and/or snow cover are obtained for most regional areas except over the Arctic and Antarctic where the secular trend is the prevailing temporal variability.Apart from these well-known signals, this contribution also demonstrates that the PCA is able to reveal longer periodic and a-periodic signal. A prominent example for the latter is the gravity signal of the Sumatra-Andaman earthquake in late 2004. In an attempt to isolate these signals, linear trend and annual signal are removed from the original data and the PCA is once again applied to the reduced data. For a complete overview of these results the most dominant PCA modes for the global and regional gravity field solutions are presented and discussed.
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Books on the topic "Principal components analysis (pca)"

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Dunteman, George. Principal Components Analysis. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1989. http://dx.doi.org/10.4135/9781412985475.

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Principal components analysis. Newbury Park, Calif: Sage, 1989.

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Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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Principal component analysis. New York: Springer-Verlag, 1986.

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Principal component analysis. 2nd ed. New York: Springer, 2002.

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Jolliffe, I. T. Principal component analysis. 2nd ed. New York: Springer, 2010.

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J, Dunn W., Scott D. R. 1934-, and United States. Environmental Protection Agency., eds. Principal components analysis and partial least squares regression. [Washington, D.C.?: U.S. Environmental Protection Agency, 1992.

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A user's guide to principal components. Hoboken, N.J: Wiley-Interscience, 2003.

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Jackson, J. Edward. A user's guide to principal components. New York: Wiley, 1991.

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Book chapters on the topic "Principal components analysis (pca)"

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Jöreskog, Karl G., Ulf H. Olsson, and Fan Y. Wallentin. "Principal Components (PCA)." In Multivariate Analysis with LISREL, 237–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33153-9_5.

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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Principal components analysis." In Practical R for biologists: an introduction, 194–99. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0017.

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Abstract This chapter focuses on how to conduct a principal components analysis. To conduct principal components analysis, R has two similar built-in functions prcomp and princomp in the default stats package. Other implementations can be found in various downloadable packages, e.g. the function PCA from the package FactoMineR, the function dudi.pca from the package ade4 and the function acp from the package amap. The functions prcomp and princomp employ different calculation methods but in practice the results they return will be almost identical.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "Principal components analysis." In Practical R for biologists: an introduction, 194–99. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0194.

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Abstract This chapter focuses on how to conduct a principal components analysis. To conduct principal components analysis, R has two similar built-in functions prcomp and princomp in the default stats package. Other implementations can be found in various downloadable packages, e.g. the function PCA from the package FactoMineR, the function dudi.pca from the package ade4 and the function acp from the package amap. The functions prcomp and princomp employ different calculation methods but in practice the results they return will be almost identical.
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Guebel, Daniel V., and Néstor V. Torres. "Principal Component Analysis (PCA)." In Encyclopedia of Systems Biology, 1739–43. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1276.

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Bisong, Ekaba. "Principal Component Analysis (PCA)." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 319–24. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_26.

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Ruby-Figueroa, René. "Principal Component Analysis (PCA)." In Encyclopedia of Membranes, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-40872-4_1999-1.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision, 1–4. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_649-1.

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Kurita, Takio. "Principal Component Analysis (PCA)." In Computer Vision, 636–39. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_649.

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Tripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "Principal Component Analysis (PCA)." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization, 5–16. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-2.

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Trendafilov, Nickolay, and Michele Gallo. "Principal component analysis (PCA)." In Multivariate Data Analysis on Matrix Manifolds, 89–139. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76974-1_4.

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Conference papers on the topic "Principal components analysis (pca)"

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Wang, Qianqian, Quanxue Gao, Xinbo Gao, and Feiping Nie. "Angle Principal Component Analysis." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/409.

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Recently, many ℓ1-norm based PCA methods have been developed for dimensionality reduction, but they do not explicitly consider the reconstruction error. Moreover, they do not take into account the relationship between reconstruction error and variance of projected data. This reduces the robustness of algorithms. To handle this problem, a novel formulation for PCA, namely angle PCA, is proposed. Angle PCA employs ℓ2-norm to measure reconstruction error and variance of projected da-ta and maximizes the summation of ratio between variance and reconstruction error of each data. Angle PCA not only is robust to outliers but also retains PCA’s desirable property such as rotational invariance. To solve Angle PCA, we propose an iterative algorithm, which has closed-form solution in each iteration. Extensive experiments on several face image databases illustrate that our method is overall superior to the other robust PCA algorithms, such as PCA, PCA-L1 greedy, PCA-L1 nongreedy and HQ-PCA.
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Abouhamed, Moustafa, Hesham Elmasry, and Ahmed Borayek. "Breaking New Ground: Exploring the Connection Between Drilling Parameters and Formation Properties through Advanced Multi-Source Data Pattern Analysis." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24366-ea.

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Abstract In the oil and gas industry, an extensive volume of logging data is routinely generated and acquired during operations. This data is accumulated across various phases of exploration, production, and subsequent operations. The challenge lies in extracting meaningful insights from this high-dimensional dataset. Constraints in data visualization (such as the three spatial axes, x, y, and z) and manipulation further complicate the process of drawing valuable conclusions. As a method to tackle this challenge, a technique has been devised to streamline data visualization by reducing high-dimensional data to a lower dimensionality, while retaining vital features crucial for big data analysis. Principal component analysis (PCA) emerges as a prominent approach employed for data dimensionality reduction. PCA simplifies multivariate datasets encompassing well-logging tools (e.g., gamma ray, density, neutron porosity, and resistivity) and drilling parameters such as rate of penetration, weight on bit, and vibration data, condensing them into a reduced number of factors known as principal components (PCs). The results of the dimensionality reduction technique show that various logging data and drilling parameters were condensed into a set of principal components (PC1, PC2…PCx), where x corresponds to the number of utilized variables. The analysis indicates that the first two components (PC1, PC2) capture most of data patterns. The graphical representation of these components (PCA biplot) reveals distinct clusters with clear patterns, facilitating the identification of separate electro-facies. Moreover, PC1 exhibits a strong correlation with lithology variations, enabling its utilization in well-to-well correlation. Additionally, regression analysis demonstrates a significant predictive relationship between PCA components and well logging variables, allowing the use of the R-squared regression technique to forecast a result curve based on PCA input. Higher principal components are found to be more associated with formation fluid, thus complementing the standard petrophysical analysis. The industry's current shift is from data collection to practical applications. By employing diverse dimensionality reduction techniques, the workflow enables the analysis of big data in a more comprehensive manner, unveiling concealed trends and insights. This approach not only facilitates machine-learning and artificial-intelligence applications but also enables a deeper understanding of the data through descriptive forms, thereby supporting industry advancements.
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Sankar, D. Sandeep Vara, and Lakshi Prosad Roy. "Principal component analysis (PCA) approach to segment primary components from pathological phonocardiogram." In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6949976.

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Schmeelk, Suzanna, and John Schmeelk. "Image authenticity implementing Principal Component Analysis (PCA)." In 2013 10th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT). IEEE, 2013. http://dx.doi.org/10.1109/cewit.2013.6713751.

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Tonshal, Basavaraj, Yifan Chen, and Pietro Buttolo. "Determine Mesh Orientation by Voxel-Based Principal Component Analysis." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99380.

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In this paper we propose a new method to determine the part orientation of a 3D mesh based on Principal Component Analysis (PCA). Although the idea and practice of using PCA to determine part orientation is not new, it is not without practical issues. A major drawback of PCA, when it comes to dealing with meshes comprised of nodes and elements, is that the results are tessellation-dependent because of its sensitivity to variability. Two CAE meshes derived from the same CAD model but with different mesh node distribution characteristics, for instance, can yield different principal components. This is an undesirable outcome because the primary concern in model reorientation is shape, not the representational details of the shape. In order to reduce the influence of node characteristics, weight factors were proposed in the past, but the improvement is limited. To overcome this limitation, we must eliminate the influence of mesh node distribution. We achieve this by introducing an intermediate workspace, which is subsequently voxelized. We then find the intersection of the mesh model with the voxelized workspace. We collect the intersecting voxels to form an intermediate, tessellation-independent representation of the mesh. Applying PCA to this “neutralized” representation allows us to achieve mesh-property-independent results. The voxel representation also provides an opportunity of computational efficiency. We implemented an octree data structure to store the voxels and implemented a fast intersection (between a mesh element and a voxel) check procedure utilizing the interval overlap check derived from the separating axis theorem. Practical issues concerning determination of the voxel space resolution is addressed. A two-step trial and correction approach is proposed to enhance the consistency of results. Our voxel-based PCA is robust, fast, and straightforward to implement. Application examples are shown demonstrating the effectiveness and efficiency of this approach.
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Zhang, Qingqing. "Principal Component Analysis (PCA) in Smart Growth Theory." In Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/ammee-17.2017.96.

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Qiu, Caihua, and Feng Ding. "Face recognition based on principal component analysis (PCA)." In 2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM). IEEE, 2022. http://dx.doi.org/10.1109/aiam57466.2022.00185.

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Sharipuddin, Benni Purnama, Kurniabudi, Eko Arip Winanto, Deris Stiawan, Darmawiiovo Hanapi, Mohd Yazid bin Idris, and Rahmat Budiarto. "Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)." In 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). IEEE, 2020. http://dx.doi.org/10.23919/eecsi50503.2020.9251292.

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Li, Liming, and Jing Zhao. "Comprehensive Evaluation of Parallel Mechanism and Robot Performance Based on Principal Component Analysis and Kernel Principal Component Analysis." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47032.

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Revealing the relations among parallel mechanism and robot comprehensive performance, topological structure and dimension is the basis to optimize mechanism. Due to the correlation and diversity of the single performance indexes, statistical principles of linear dimension reduction and nonlinear dimension reduction were introduced into comprehensive performance analysis and evaluation for typical parallel mechanisms and robots. Then the mechanism’s topological structure and dimension with the best comprehensive performance could be selected based on principal component analysis (PCA) and kernel principal component analysis (KPCA) respectively. Through comparing the results, KPCA could reveal the nonlinear relationship among different single performance indexes to provide more comprehensive performance evaluation information than PCA, and indicate the numerical calculation relations among comprehensive performance, topological structure and dimension validly.
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Goldberg, Mitchell D., Lihang Zhou, Walter W. Wolf, Chris Barnet, and Murty G. Divakarla. "Applications of principal component analysis (PCA) on AIRS data." In Multispectral and Hyperspectral Remote Sensing Instruments and Applications II. SPIE, 2005. http://dx.doi.org/10.1117/12.578939.

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Reports on the topic "Principal components analysis (pca)"

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Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan, and Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2005. http://dx.doi.org/10.55274/r0012049.

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In this project, Intelligent Automation, Incorporated (IAI) and Oak Ridge National Lab (ORNL) propose a novel and integrated approach to inspect the mechanical dents and metal loss in pipelines. It combines the state-of-the-art SH wave Electromagnetic Acoustic Transducer (EMAT) technique, through detailed numerical modeling, data collection instrumentation, and advanced signal processing and pattern classifications, to detect and characterize mechanical defects in the underground pipeline transportation infrastructures. The technique has four components: (1) thorough guided wave modal analysis, (2) recently developed three-dimensional (3-D) Boundary Element Method (BEM) for best operational condition selection and defect feature extraction, (3) ultrasonic Shear Horizontal (SH) waves EMAT sensor design and data collection, and (4) advanced signal processing algorithm like a nonlinear split-spectrum filter, Principal Component Analysis (PCA) and Discriminant Analysis (DA) for signal-to-noise-ratio enhancement, crack signature extraction, and pattern classification. This technology not only can effectively address the problems with the existing methods, i.e., to detect the mechanical dents and metal loss in the pipelines consistently and reliably but also it is able to determine the defect shape and size to a certain extent.
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Corriveau, Elizabeth, Travis Thornell, Mine Ucak-Astarlioglu, Dane Wedgeworth, Hayden Hanna, Robert Jones, Alison Thurston, and Robyn Barbato. Characterization of pigmented microbial isolates for use in material applications. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46633.

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Organisms (i.e., plants and microorganisms) contain pigments that allow them to adapt and thrive under stressful conditions, such as elevated ultraviolet radiation. The pigments elicit characteristic spectral responses when measured by active and passive sensors. This research study focused on characterizing the spectral response of three organisms and how they compared to background spectral signatures of a complex environment. Specifically, spectra were collected from a fungus, a plant, and two pigmented bacteria, one of which is an extremophile bacterium. The samples were measured using Fourier transform infrared spectroscopy and dis-criminated using chemometric means. A top-down examination of the spectral data revealed that organisms could be discriminated from one an-other through principal component analysis (PCA). Furthermore, there was a strong distinction between the plant and the pigmented microorganisms. Spectral differences resulting in samples with the highest variance from the natural background were identified using PCA loading plots. The outcome of this work is a spectral library of pigmented biological candidates for coatings applications.
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Diba, Dil Samina, Ninad Gore, and Srinivas Pulugurtha. Autonomous Shuttle Implementation and Best Practices. Mineta Transportation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2321.

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When, where, and how autonomous shuttles are deployed can have significant safety, economic, and policy impacts on their operation and performance. This research analyzes data related to 120 existing deployments of autonomous shuttles, looking at safety, operational, economic, and policy-related issues. Analysis shows that autonomous shuttles would be an excellent supplement to public transportation. However, improvements to the vehicle and the infrastructure are needed before any permanent deployment. The study also analyzes the perceptions of practitioners, industry experts, and transportation system users toward autonomous shuttles. Principal Component Analysis (PCA) and Multiple Input Multiple Cause Structural Equation Modelling (SEM) approaches were adopted to analyze the perception data. The results from the PCA highlighted critical barriers to autonomous shuttle implementation, including underutilization measures, safety concerns, seating arrangements, reliability, data security, operational aspects, sensor technology, and lane use. The results from the SEM revealed that users’ willingness to use autonomous shuttles is influenced by their perceived safety, comfort, trust in autonomous shuttles, familiarity with autonomous shuttles, household income, age, and frequency of transit usage. A set of recommended best practices for deploying autonomous shuttles is proposed based on the insights from multiple case studies and the perceptions of practitioners and industry experts.
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Zandiatashbar, Ahoura, Jochen Albrecht, and Hilary Nixon. A Bike System for All in Silicon Valley: Equity Assessment of Bike Infrastructure in San José, CA. Mineta Transportation Institute, October 2023. http://dx.doi.org/10.31979/mti.2023.2162.

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Investing in sustainable, multimodal infrastructure is of increasing importance throughout the United States and worldwide. Cities are increasingly making strategic capital investment decisions about bicycle infrastructure—decisions that need planning efforts that accurately assess the equity aspects of developments, achieve equitable distribution of infrastructures, and draw upon accurate assessment methods. Toward these efforts, this project uses a granular bike network dataset with statistical and geospatial analyses to quantify a bike infrastructure availability score (i.e., bike score) that accounts for the safety and comfort differences in bike path classes in San José, California. San José is the 10th largest U.S. city and a growing tech hub with a booming economy, factors that correlate with increased traffic congestion if adequate multimodal and active transportation infrastructure are not in place. Therefore, San José has been keen on becoming “one of the most bike-friendly communities in North America.” The City’s new plan, which builds on its first bike plan adopted in 2009, envisions a 557-mile network of allages-and-abilities bikeways to support a 20% bicycle mode split (i.e., 20% of all trips to be made by bike) by 2050. Hence, San José makes a perfect study area for piloting this project’s methodology for accurately assessing the equity of urban bike plans and infrastructures. The project uses the above-mentioned bike score (representing the bike infrastructure supply status) and San José residents’ bike travel patterns (to show bike trip demand status) utilizing StreetLight data to answer the following questions: (1) Where are San José's best (bike paradise) and worst (bike desert) regions for cycling? (2) How different are the socioeconomic attributes of San José’s bike desert and paradise residents? (3) Has San José succeeded in achieving an equitable infrastructure distribution and, if so, to what extent? And, (4) has the availability of infrastructure attracted riders from underserved communities and, if so, to what extent? Using the bike infrastructure availability score, this research measures and maps the City of San José's best and worst regions for cycling through geospatial analyses to answer Question 1 above. Further spatial and statistical analyses including t-tests, Pairwise Pearson correlation analysis, descriptive analysis, spatial visualization, principal component analysis (PCA), and multiple regression models to answer Questions 2, 3, and 4. In addition to this report, the findings are used to develop an open access web-tool, the San José Bike Equity Web Map (SJ-BE iMap). This research contributes to the critical assessment and planning efforts of sustainable, multimodal infrastructure in California and beyond.
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Ayres, João, Arturo Galindo, Santiago Novoa, and Victoria Nuguer. Inflation Dynamics in Latin America and the Caribbean. Inter-American Development Bank, March 2023. http://dx.doi.org/10.18235/0004751.

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We perform a principal component analysis of the inflation dynamics in Latin America and the Caribbean to assess the recent surge in inflation across the region. The principal component accounts for 57% of the variation in inflation in the last 17 years, and it is highly correlated to the principal components of inflation of country groups outside the region, especially post-COVID-19. Global factors such as US inflation, commodity prices, and international shipping costs can account for at least one-third of the variation of the principal component. The analysis implies that external factors are major drivers of the surge in inflation in the region post-COVID-19 lockdowns.
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Harter, Rachel M., Pinliang (Patrick) Chen, Joseph P. McMichael, Edgardo S. Cureg, Samson A. Adeshiyan, and Katherine B. Morton. Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey. RTI Press, May 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.

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The 2015 Residential Energy Consumption Survey design called for stratification of primary sampling units to improve estimation. Two methods of defining strata from multiple stratification variables were proposed, leading to this investigation. All stratification methods use stratification variables available for the entire frame. We reviewed textbook guidance on the general principles and desirable properties of stratification variables and the assumptions on which the two methods were based. Using principal components combined with cluster analysis on the stratification variables to define strata focuses on relationships among stratification variables. Decision trees, regressions, and correlation approaches focus more on relationships between the stratification variables and prior outcome data, which may be available for just a sample of units. Using both principal components/cluster analysis and decision trees, we stratified primary sampling units for the 2009 Residential Energy Consumption Survey and compared the resulting strata.
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Velez, Gladis, and Ragvi Shah. Reorienting Smart City Metrics to Emphasize Resident Well-Being: A Disparity-Oriented Approach. University of Miami, 2022. http://dx.doi.org/10.33596/report-1.

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This paper applies a disparity-oriented focus to promote human-centered solutions to smart city planning efforts. For five metropolitan areas (San Jose, Miami, New York, Denver, and Seattle) we explored three smart city domains (socioeconomics, public transit access, and digital divide), identified candidate indicators for each domain using publicly available data, and mapped composite measures generated using principal components analysis. The study identifies areas that may be most and least likely to benefit from smart city investments. Reorienting solutions can ultimately increase community equity and engagement in urban life.
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Beltrão, Kaizô I., Rosa M. R. Massena, and David M. Vetter. The Impact of the Sense of Security from Crime on Residential Property Values in Brazilian Metropolitan Areas. Inter-American Development Bank, June 2013. http://dx.doi.org/10.18235/0011493.

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Using a hedonic residential rent model for Brazil's metropolitan areas calibrated with microdata from Brazil's annual household survey, this study estimates that increasing the sense of security in the home by one standard deviation would increase average home values by R$1,513 (US$757), or about US$13. 6 billion if applied to all 18.0 million households in the study area. The principal components analysis of sense of security and crime victimization variables indicates that higher-income households feel more secure from crime in the home, even though theft and robbery victimization rise with household income and rent level. Higher levels of home protection measures by higher-income households partially explain this result.
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Filmer, Deon, Ezequiel Molina, and Waly Wane. Identifying Effective Teachers: Lessons from Four Classroom Observation Tools. Research on Improving Systems of Education (RISE), August 2020. http://dx.doi.org/10.35489/bsg-rise-wp_2020/045.

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Four different classroom observation instruments—from the Service Delivery Indicators, the Stallings Observation System, the Classroom Assessment Scoring System, and the Teach classroom observation instrument—were implemented in about 100 schools across four regions of Tanzania. The research design is such that various combinations of tools were administered to various combinations of teachers, so these data can be used to explore the commonalities and differences in the behaviors and practices captured by each tool, the internal properties of the tools (for example, how stable they are across enumerators, or how various indicators relate to one another), and how variables collected by the various tools compare to each other. Analysis shows that inter-rater reliability can be low, especially for some of the subjective ratings; principal components analysis suggests that lower-level constructs do not map neatly to predetermined higher-level ones and suggest that the data have only a few dimensions. Measures collected during teacher observations are associated with student test scores, but patterns differ for teachers with lower versus higher subject content knowledge.
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Balali, Vahid. System-of-Systems Integration for Civil Infrastructures Resiliency Toward MultiHazard Events. Mineta Transportation Institute, August 2023. http://dx.doi.org/10.31979/mti.2023.2245.

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Civil infrastructure systems—facilities that supply principal services, such as electricity, water, transportation, etc., to a community—are the backbone of modern society. These systems are frequently subject to multi-hazard events, such as earthquakes. The poor resiliency of these infrastructures results in many human casualties and significant economic losses every year. An outline of a holistic view that considers how different civil infrastructure systems operate independently and how they interact and communicate with each other is required to have a resilient infrastructure system. More specifically a systems engineering approach is required to enable infrastructure to remain resilient in the case of extreme events, including natural disasters. To address these challenges, this research builds on the proposal that the infrastructure systems be equipped with state-of-the-art sensor networks that continuously record the condition and performance of the infrastructure. The sensor data from each infrastructure are then transferred to a data analysis system component that employs artificial intelligence techniques to constantly analyze the infrastructure’s resiliency and energy efficiency performance. This research models the resilient infrastructure problem as a System of Systems (SoS) comprised of the abovementioned components. It explores system integration and operability challenges and proposes solutions to meet the requirements of the SoS. An integration ontology, as well as a data-centric architecture, is developed to enable infrastructure resiliency toward multi-hazard events. The Federal Emergency Management Agency (FEMA), and infrastructure managers, such as Departments of Transportation (DOTs) and the Federal Highway Administration (FHWA), can learn from and integrate these solutions to make civil infrastructure systems more resilient for all.
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