Relevant bibliographies by topics / Principal Component Analysis Atlas

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Principal Component Analysis Atlas'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Principal Component Analysis Atlas"

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J., Ravi *. M. Praveen Kumar N. Kishore Chandra Dev. "SEGMENTATION OF 3D MR IMAGES OF THE BRAIN USING A PCA ATLAS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 10 (2017): 288–96. https://doi.org/10.5281/zenodo.1012497.

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This Paper represents a method for the automatic segmentation of the brain in magnetic resonance (MR) images of the human head. The method identifies brain areas of interest, including the gyri and other subcortical structures that were manually delineated in a set of labelled training images. Principal components analysis (PCA) is applied to the training ensemble in order to learn a PCA atlas subspace, which is a dimensionality-reduced linear subspace of labelled brain images. We employ this subspace to segment and label previously unseen subject images. This is accomplished by finding the PC
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Meseke, Christopher A., Stephen M. Duray, and Sebastien R. Brillon. "Principal Components Analysis of the Atlas Vertebra." Journal of Manipulative and Physiological Therapeutics 31, no. 3 (2008): 212–16. http://dx.doi.org/10.1016/j.jmpt.2008.02.010.

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Lima, H. P., and J. M. Seixas. "A segmented principal component analysis applied to calorimetry information at ATLAS." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 559, no. 1 (2006): 129–33. http://dx.doi.org/10.1016/j.nima.2005.11.131.

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Harada, Nanase, David S. Meier, Sergio Martín, et al. "The ALCHEMI Atlas: Principal Component Analysis Reveals Starburst Evolution in NGC 253." Astrophysical Journal Supplement Series 271, no. 2 (2024): 38. http://dx.doi.org/10.3847/1538-4365/ad1937.

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Abstract Molecular lines are powerful diagnostics of the physical and chemical properties of the interstellar medium (ISM). These ISM properties, which affect future star formation, are expected to differ in starburst galaxies from those of more quiescent galaxies. We investigate the ISM properties in the central molecular zone of the nearby starburst galaxy NGC 253 using the ultrawide millimeter spectral scan survey from the Atacama Large Millimeter/submillimeter Array Large Program ALCHEMI. We present an atlas of velocity-integrated images at a 1.″6 resolution of 148 unblended transitions fr
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Brigitta ersa pancarwani, Maherawati, and Oke Anandika Lestari. "ANALISIS INDIKATOR KETAHANAN PANGAN KABUPATEN MELAWI DENGAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA)." Jurnal Agroindustri Pangan 4, no. 1 (2025): 57–71. https://doi.org/10.47767/agroindustri.v4i1.980.

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Ketahanan pangan merupakan situasi ketika setiap rumah tangga memiliki kapasitas fisik atau ekonomi yang memadai untuk mengakses pangan bagi semua anggota keluarganya. Kabupaten Melawi menjadi salah satu kabupaten di Kalimantan Barat yang mempunyai kecamatan rentan pangan berdasarkan hasil analisis FSVA (Food Security and Vulnerability Atlas) tahun 2022. Tujuan penelitian untuk mendapatkan indikator-indikator penting yang mempengaruhi kondisi ketahanan pangan di Kabupaten Melawi dengan metode analisis PCA. Hasil dari penelitian ini diketahui terdapat lima variabel penting mempengaruhi ketahana
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Asensi Tortajada, Ignacio, André Rummler, George Salukvadze, Carlos Solans Sánchez, and Kendall Reeves. "ATLAS Technical Coordination Expert System." EPJ Web of Conferences 214 (2019): 05035. http://dx.doi.org/10.1051/epjconf/201921405035.

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When planning an intervention on a complex experiment like ATLAS, the detailed knowledge of the system under intervention and of the interconnection with all the other systems is mandatory. In order to improve the understanding of the parties involved in an intervention, a rule-based expert system has been developed. On the one hand this helps to recognise dependencies that are not always evident and on the other hand it facilitates communication between experts with different backgrounds by translating between vocabularies of specific domains. To simulate an event this tool combines informati
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Park, Mira, Doyoen Kim, Kwanyoung Moon, and Taesung Park. "Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components." International Journal of Molecular Sciences 21, no. 21 (2020): 8202. http://dx.doi.org/10.3390/ijms21218202.

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The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analy
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Hanh, Nguyen Hong, Nguyen Thi Ngoc Dinh, Pham Thi Thu Huong, et al. "Variation of morpho-agronomical characteristics and genetic diversity of imported asparagus cultivars in Vietnam." February 2024, no. 18(02):2024 (February 1, 2024): 107–15. http://dx.doi.org/10.21475/ajcs.24.18.02.pne4101.

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Asparagus (Asparagus officinalis L.) is a perennial plant globally known for its unique texture, good flavor, and high nutritional value. This paper aims to provide information on the phenotypic traits, the situation of stem blight disease of the imported asparagus varieties and introduce suitable asparagus cultivars for Vietnam. The evaluation of morpho-agronomical characteristics variability of 13 imported asparagus cultivars in the first growing year was arranged in a Random Complete Block Design (RCBD) with 3 replicates in Hanoi, Vietnam. Genetic diversity was assessed using hierarchical c
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Cosentino, Federica, Giuseppe M. Raffa, Giovanni Gentile, et al. "Statistical Shape Analysis of Ascending Thoracic Aortic Aneurysm: Correlation between Shape and Biomechanical Descriptors." Journal of Personalized Medicine 10, no. 2 (2020): 28. http://dx.doi.org/10.3390/jpm10020028.

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An ascending thoracic aortic aneurysm (ATAA) is a heterogeneous disease showing different patterns of aortic dilatation and valve morphologies, each with distinct clinical course. This study aimed to explore the aortic morphology and the associations between shape and function in a population of ATAA, while further assessing novel risk models of aortic surgery not based on aortic size. Shape variability of n = 106 patients with ATAA and different valve morphologies (i.e., bicuspid versus tricuspid aortic valve) was estimated by statistical shape analysis (SSA) to compute a mean aortic shape an
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Li, Mr Yonghao, Mr Yizhou Wan, and Prof Stephen Price. "INTEGRATING TRANSCRIPTOMICS WITH SPATIAL RADIOGRAPHIC ATLASES FOR GLIOMA ANALYSIS." Neuro-Oncology 26, Supplement_7 (2024): vii10. http://dx.doi.org/10.1093/neuonc/noae158.039.

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Abstract AIMS The localization of gliomas in the brain is a critical prognostic factor, reflecting the genetic makeup of their originating cells. Variations in transcriptomic profiles across different regions and subtypes may further shed light on the mechanisms driving tumor development. Our research delves into the complex interplay between glioma locations and their transcriptomic signatures, seeking to dissect the spatial and genetic intricacies contributing to glioma diversity. METHOD In this study, we conducted a comprehensive analysis of preoperative anatomical MRI and transcriptomic data
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Dissertations / Theses on the topic "Principal Component Analysis Atlas"

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Casero, Cañas Ramón. "Left ventricle functional analysis in 2D+t contrast echocardiography within an atlas-based deformable template model framework." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:b17b3670-551d-4549-8f10-d977295c1857.

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This biomedical engineering thesis explores the opportunities and challenges of 2D+t contrast echocardiography for left ventricle functional analysis, both clinically and within a computer vision atlas-based deformable template model framework. A database was created for the experiments in this thesis, with 21 studies of contrast Dobutamine Stress Echo, in all 4 principal planes. The database includes clinical variables, human expert hand-traced myocardial contours and visual scoring. First the problem is studied from a clinical perspective. Quantification of endocardial global and local funct
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Kpamegan, Neil Racheed. "Robust Principal Component Analysis." Thesis, American University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10784806.

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<p> In multivariate analysis, principal component analysis is a widely popular method which is used in many different fields. Though it has been extensively shown to work well when data follows multivariate normality, classical PCA suffers when data is heavy-tailed. Using PCA with the assumption that the data follows a stable distribution, we will show through simulations that a new method is better. We show the modified PCA can be used for heavy-tailed data and that we can more accurately estimate the correct number of components compared to classical PCA and more accurately identify the subs
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Akinduko, Ayodeji Akinwumi. "Multiscale principal component analysis." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/36616.

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The problem of approximating multidimensional data with objects of lower dimension is a classical problem in complexity reduction. It is important that data approximation capture the structure(s) and dynamics of the data, however distortion to data by many methods during approximation implies that some geometric structure(s) of the data may not be preserved during data approximation. For methods that model the manifold of the data, the quality of approximation depends crucially on the initialization of the method. The first part of this thesis investigates the effect of initialization on manif
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Der, Ralf, Ulrich Steinmetz, Gerd Balzuweit, and Gerrit Schüürmann. "Nonlinear principal component analysis." Universität Leipzig, 1998. https://ul.qucosa.de/id/qucosa%3A34520.

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We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organizing maps. We present a general algorithm which maps low-dimensional lattices into high-dimensional data manifolds without violation of topology. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. Moreover we present a second algorithm for the extraction of generalized principal curves comprising disconnected and branching manifolds. The performance of the algorithm is demonstrated for both
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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'
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Fučík, Vojtěch. "Principal component analysis in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.

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The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by
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Wedlake, Ryan Stuart. "Robust principal component analysis biplots." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.

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Brennan, Victor L. "Principal component analysis with multiresolution." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.

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Thesis (Ph. D.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).
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Cadima, Jorge Filipe Campinos Landerset. "Topics in descriptive Principal Component Analysis." Thesis, University of Kent, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314686.

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Isaac, Benjamin. "Principal component analysis based combustion models." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209278.

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Energy generation through combustion of hydrocarbons continues to dominate, as the most common method for energy generation. In the U.S. nearly 84% of the energy consump- tion comes from the combustion of fossil fuels. Because of this demand there is a continued need for improvement, enhancement and understanding of the combustion process. As computational power increases, and our methods for modelling these complex combustion systems improve, combustion modelling has become an important tool in gaining deeper insight and understanding for these complex systems. The constant state of change in
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Books on the topic "Principal Component Analysis Atlas"

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Jolliffe, I. T. Principal Component Analysis. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1904-8.

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

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Vidal, René, Yi Ma, and S. S. Sastry. Generalized Principal Component Analysis. Springer New York, 2016. http://dx.doi.org/10.1007/978-0-387-87811-9.

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Naik, Ganesh R., ed. Advances in Principal Component Analysis. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6704-4.

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Sanguansat, Parinya. Principal component analysis - multidisciplinary applications. InTech, 2012.

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Hyvarinen, Aapo. Independent component analysis. J. Wiley, 2001.

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Juha, Karhunen, and Oja Erkki, eds. Independent component analysis. J. Wiley, 2001.

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Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2915-8.

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Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. Nonlinear Principal Component Analysis and Its Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0159-8.

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D, Mobley Curtis, ed. Principal component analysis in meteorology and oceanography. Elsevier, 1988.

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Book chapters on the topic "Principal Component Analysis Atlas"

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Deutsch, Hans-Peter, and Mark W. Beinker. "Principal Component Analysis." In Derivatives and Internal Models. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22899-6_34.

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Forsyth, David. "Principal Component Analysis." In Applied Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18114-7_5.

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Ma, Y. Z. "Principal Component Analysis." In Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17860-4_5.

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Filzmoser, Peter, Karel Hron, and Matthias Templ. "Principal Component Analysis." In Springer Series in Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96422-5_7.

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Du, Ke-Lin, and M. N. S. Swamy. "Principal Component Analysis." In Neural Networks and Statistical Learning. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5571-3_12.

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Chiang, Leo H., Evan L. Russell, and Richard D. Braatz. "Principal Component Analysis." In Fault Detection and Diagnosis in Industrial Systems. Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0347-9_4.

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Russell, Evan L., Leo H. Chiang, and Richard D. Braatz. "Principal Component Analysis." In Advances in Industrial Control. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0409-4_4.

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Flury, Bernhard, and Hans Riedwyl. "Principal component analysis." In Multivariate Statistics. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1217-5_10.

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Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Principal Component Analysis." In Multivariate Statistics. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_11.

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Du, Ke-Lin, and M. N. S. Swamy. "Principal Component Analysis." In Neural Networks and Statistical Learning. Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-7452-3_13.

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Conference papers on the topic "Principal Component Analysis Atlas"

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Li, Wenjun, John Kornak, Tamara Harris, Ying Lu, Xiaoguang Cheng, and Thomas Lang. "Hip fracture risk estimation based on principal component analysis of QCT atlas: a preliminary study." In SPIE Medical Imaging, edited by Xiaoping P. Hu and Anne V. Clough. SPIE, 2009. http://dx.doi.org/10.1117/12.811743.

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Zhao, Yu, Hongwei Li, Rong Zhou, Giles Tetteh, Marc Niethammer, and Bjoern H. Menze. "Automatic Multi-Atlas Segmentation for Abdominal Images Using Template Construction and Robust Principal Component Analysis." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546323.

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Tang, F., and H. Tao. "Binary Principal Component Analysis." In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.39.

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Siirtola, Harri, Tanja Saily, and Terttu Nevalainen. "Interactive Principal Component Analysis." In 2017 21st International Conference on Information Visualisation (IV). IEEE, 2017. http://dx.doi.org/10.1109/iv.2017.39.

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Hsu, Charles, and Harold Szu. "Sequential principal component analysis." In SPIE Defense, Security, and Sensing, edited by Harold Szu. SPIE, 2011. http://dx.doi.org/10.1117/12.887509.

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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. 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
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Pimentel-Alarcon, Daniel L., Aritra Biswas, and Claudia R. Solis-Lemus. "Adversarial principal component analysis." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006952.

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Sehgal, Shruti, Harpreet Singh, Mohit Agarwal, V. Bhasker, and Shantanu. "Data analysis using principal component analysis." In 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom). IEEE, 2014. http://dx.doi.org/10.1109/medcom.2014.7005973.

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Wojnowicz, Michael, Dinh Nguyen, Li Li, and Xuan Zhao. "Lazy Stochastic Principal Component Analysis." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.79.

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Pei, Yan. "Linear Principal Component Discriminant Analysis." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.368.

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Reports on the topic "Principal Component Analysis Atlas"

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MARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/810934.

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Hamilton, James, and Jin Xi. Principal Component Analysis for Nonstationary Series. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32068.

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Aït-Sahalia, Yacine, and Dacheng Xiu. Principal Component Analysis of High Frequency Data. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21584.

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Eick, Brian, Zachary Treece, Billie Spencer, et al. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), 2018. http://dx.doi.org/10.21079/11681/27365.

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Federer, W. T., C. E. McCulloch, and J. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis Using SYSTAT/FACTOR. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada184920.

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Federer, W. T., C. E. McCulloch, and N. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis using BMDP/4M. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada185179.

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Krishnaiah, P. R., and S. Sarkar. Principal Component Analysis Under Correlated Multivariate Regression Equations Model. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160266.

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Thompson, David C., Janine C. Bennett, Diana C. Roe, and Philippe Pierre Pebay. Scalable multi-correlative statistics and principal component analysis with Titan. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/984172.

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Fujikoshi, Y., P. R. Krishnaiah, and J. Schmidhammer. Effect of Additional Variables in Principal Component Analysis, Discriminant Analysis and Canonical Correlation Analysis. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162069.

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Thompson, David, Ray W. Grout, Nathan D. Fabian, and Janine Camille Bennett. Detecting Combustion and Flow Features In Situ Using Principal Component Analysis. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/1324759.

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