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Journal articles on the topic 'Principle component analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Wen, Chenglin, Tianzhen Wang, and Jing Hu. "Relative principle component and relative principle component analysis algorithm." Journal of Electronics (China) 24, no. 1 (2007): 108–11. http://dx.doi.org/10.1007/s11767-006-0097-2.

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2

Polyak, B. T., and M. V. Khlebnikov. "Principle component analysis: Robust versions." Automation and Remote Control 78, no. 3 (2017): 490–506. http://dx.doi.org/10.1134/s0005117917030092.

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3

Chen, Songcan, and Yulian Zhu. "Subpattern-based principle component analysis." Pattern Recognition 37, no. 5 (2004): 1081–83. http://dx.doi.org/10.1016/j.patcog.2003.09.004.

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4

Wu, Danyang, Han Zhang, Feiping Nie, et al. "Double-Attentive Principle Component Analysis." IEEE Signal Processing Letters 27 (2020): 1814–18. http://dx.doi.org/10.1109/lsp.2020.3027462.

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5

Arab, Abbas, Jamila Harbi, and Amel Abbas. "Image Compression Using Principle Component Analysis." Al-Mustansiriyah Journal of Science 29, no. 2 (2018): 141. http://dx.doi.org/10.23851/mjs.v29i2.256.

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Principle component analysis produced reduction in dimension, therefore in our proposed method used PCA in image lossy compression and obtains the quality performance of reconstructed image. PSNR values increase when the number of PCA components is increased and CR, MSE, and other error parameters decreases when the number of components is increased.

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Zhang, Yan, and Shi Sheng Zhou. "Research on Reconstruction of Spectral Reflectance Based on Principal Component Analysis." Applied Mechanics and Materials 262 (December 2012): 53–58. http://dx.doi.org/10.4028/www.scientific.net/amm.262.53.

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Traditional color reproduction technology based on the Metamerism principle, the disadvantage is that different observer condition leads to different color appearance.To fulfill the color consistency, the spectrum reflectance of the object color sample need to be reconstructed. The principal component analysis makes use of the linear combination of a few principal components to reconstruct the spectral reflectance of sample. This paper analyzes the 31*31 matrix of Munsell spectral data by the principle component analyze method and achieves the principal component for spectrum reflectance. The

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Dholakia, Stuti G., and Chetna D. Bhavsar. "Factor recovery by principle component analysis and harris component analysis." Asian Journal of Research in Social Sciences and Humanities 7, no. 7 (2017): 177. http://dx.doi.org/10.5958/2249-7315.2017.00376.8.

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8

Kim, Je-Nam, Mun-Ho Ryu, Ho-Rim Choi, and Yoon-Seok Yang. "Anatomy Calibration of Inertial Measurement Unit Using a Principle Component Analysis." International Journal of Bio-Science and Bio-Technology 5, no. 6 (2013): 181–90. http://dx.doi.org/10.14257/ijbsbt.2013.5.6.19.

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9

Bowden, R., T. A. Mitchell, and M. Sarhadi. "Cluster based nonlinear principle component analysis." Electronics Letters 33, no. 22 (1997): 1858. http://dx.doi.org/10.1049/el:19971300.

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10

B S, Lokasree. "Data Analysis and Data Classification in Machine Learning using Linear Regression and Principal Component Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 835–44. http://dx.doi.org/10.17762/turcomat.v12i2.1092.

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In this paper step-by-step procedure to implement linear regression and principal component analysis by considering two examples for each model is explained, to predict the continuous values of target variables. Basically linear regression methods are widely used in prediction, forecasting and error reduction. And principle component analysis is applied for facial recognition, computer vision etc. In Principal component analysis, it is explained how to select a point with respect to variance. And also Lagrange multiplier is used to maximize the principle component function, so that optimized s

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Aini Abdul Wahab, Nurul, and Shamshuritawati Sharif. "Rice Odours’ Readings Investigation Using Principal Component Analysis." International Journal of Engineering & Technology 7, no. 2.29 (2018): 488. http://dx.doi.org/10.14419/ijet.v7i2.29.13803.

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The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important

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12

Bischoff, Petra, Eckehard Scharein, Gunter N. Schmidt, Georg von Knobelsdorff, Burkhart Bromm, and Jochen Schulte am Esch. "Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis." Anesthesiology 92, no. 6 (2000): 1545–52. http://dx.doi.org/10.1097/00000542-200006000-00010.

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Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 lea

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13

Uzzafer, Masood. "Principle Component Analysis of Function Point Elements." International Journal of Advanced Science and Technology 91 (July 31, 2016): 39–48. http://dx.doi.org/10.14257/ijast.2016.91.04.

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14

Shams, Mohamed A. Bin. "Fault Identification using Kernel Principle Component Analysis." IFAC Proceedings Volumes 44, no. 1 (2011): 4320–25. http://dx.doi.org/10.3182/20110828-6-it-1002.03747.

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15

Kasban, H., H. Arafa, and S. M. S. Elaraby. "Principle component analysis for radiotracer signal separation." Applied Radiation and Isotopes 112 (June 2016): 20–26. http://dx.doi.org/10.1016/j.apradiso.2016.03.005.

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Purwandari, Endina Putri, Aan Erlansari, Andang Wijanarko, and Erich Adinal Adrian. "Face sketch recognition using principal component analysis for forensics application." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (2020): 178–84. http://dx.doi.org/10.14710/jtsiskom.2020.13422.

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Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the C

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Raffey, Dr Mohd Abdul. "Sentimental Analysis of Tweets using Principle Component Analysis Technique." International Journal for Research in Applied Science and Engineering Technology V, no. XI (2017): 1601–4. http://dx.doi.org/10.22214/ijraset.2017.11229.

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18

Yong, Ching Yee, Rubita Sudirman, Nasrul Humaimi Mahmood, and Kim Mey Chew. "Human Hand Movement Analysis Using Principle Component Analysis Classifier." Applied Mechanics and Materials 284-287 (January 2013): 3126–30. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3126.

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This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. It was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum scatt

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19

Wang, Zhiliang, Yalin Sun, and Peng Li. "Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index." Discrete Dynamics in Nature and Society 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/365204.

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The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA). Functional data analysis (FDA) deals with random variables (or process) with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistica

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20

Xu, Xiaoming, and Chenglin Wen. "Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis." Journal of Control Science and Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2697297.

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In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variab

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21

Dong, Jian Chao, Tie Jun Yang, Xin Hui Li, Zhi Jun Shuai, and You Hong Xiao. "Identification of Excitation Source Number Using Principal Component Analysis." Advanced Materials Research 199-200 (February 2011): 850–57. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.850.

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Principal component analysis (PCA), serving as one of the basic blind signal processing techniques, is extensively employed in all forms of analysis for extracting relevant information from confusing data sets. The principle of PCA is explained in this paper firstly, then the simulation and experiment are carried out to a simply supported beam rig, and PCA is used in frequency domain to identify sources number of several cases. Meanwhile principal components (PCs) contribution coefficient and signal to noise ratio between neighboring PCs (neighboring SNR) are introduced to cutoff minor compone

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22

SHIN, H. C., H. N. KIM, and W. J. SONG. "A Simple Adaptive Algorithm for Principle Component and Independent Component Analysis." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 5 (2008): 1265–67. http://dx.doi.org/10.1093/ietfec/e91-a.5.1265.

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23

Liu, Yong, Biao Ma, Yu Yan, and Chang Song Zheng. "Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission." Applied Mechanics and Materials 741 (March 2015): 183–87. http://dx.doi.org/10.4028/www.scientific.net/amm.741.183.

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Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle

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Holubova, Halyna. "A comparative analysis of the principal component method and parallel analysis in working with official statistical data." Statistics in Transition new series 24, no. 1 (2023): 199–212. http://dx.doi.org/10.59170/stattrans-2023-011.

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The dynamic development of the digitized society generates large-scale information data flows. Therefore, data need to be compressed in a way allowing its content to remain complete and informative. In order for the above to be achieved, it is advisable to use the principal component method whose main task is to reduce the dimension of multidimensional space with a minimal loss of information. The article describes the basic conceptual approaches to the definition of principle components. Moreover, the methodological principles of selecting the main components are presented. Among the many way

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M. Saleh, Maysoun, Abdelmoaty B. Elabd, and Abdel Rahman Mohammad Al Tawaha. "Principle Component Analysis among Exotic and Egyptian Rice Genotypes." Advances in Environmental Biology 15, no. 1 (2007): 16–22. http://dx.doi.org/10.22587/aeb.2021.15.1.3.

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Twenty-five rice genotypes with two Egyptian rice varieties: Giza 178 and Sakha 105 were all cultivated in a Randomized Complete Block Design (RCBD) with two replications under saline soil at at El_Sirw Agriculture Research Station northern part of Delta, Egypt during the growing season 2012. This study aimed to evaluate the potential divergence among tested genotypes and to define the role of agronomic traits in the total variation by using principle component analysis. Results showed that high variability was noticed between the testes genotypes, and indicated that only four principle compon

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26

Ma Yuan, L Qun-Bo, Liu Yang-Yang, Qian Lu-Lu, and Pei Lin-Lin. "Image sparsity evaluation based on principle component analysis." Acta Physica Sinica 62, no. 20 (2013): 204202. http://dx.doi.org/10.7498/aps.62.204202.

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27

Liu, Yang, Hong Li, Si-Yu Li, Yong-Ping Li, and Xinmin Zhang. "Cosmic reionization study: principle component analysis after Planck." Journal of Cosmology and Astroparticle Physics 2016, no. 02 (2016): 046. http://dx.doi.org/10.1088/1475-7516/2016/02/046.

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28

Tanaka, Masahiro, Fumiaki Takeda, and Yasuyo Michiyuki. "Bank Note Recognition by Probabilistic Principle Component Analysis." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1999 (May 5, 1999): 25–30. http://dx.doi.org/10.5687/sss.1999.25.

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29

Chakraborty, Dulal, Sanjit Kumar Saha, and Md Al-Amin Bhuiyan. "Face Recognition using Eigenvector and Principle Component Analysis." International Journal of Computer Applications 50, no. 10 (2012): 42–49. http://dx.doi.org/10.5120/7811-0947.

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30

Shi, Xiaoran, Feng Zhou, Mingliang Tao, and Zijing Zhang. "Human Movements Separation Based on Principle Component Analysis." IEEE Sensors Journal 16, no. 7 (2016): 2017–27. http://dx.doi.org/10.1109/jsen.2015.2509185.

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31

Azhari, Budi, and Ade Irfan. "MODEL-ELICITING ACTIVITIES DALAM MENGANALISIS KREATIVITAS PEMECAHAN MASALAH MATEMATIKA PADA MAHASISWA PENDIDIKAN MATEMATIKA DI PTKIN ACEH." Al Khawarizmi: Jurnal Pendidikan dan Pembelajaran Matematika 2, no. 1 (2019): 1. http://dx.doi.org/10.22373/jppm.v2i1.4495.

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This study aimed to know the components of the student creativity in problem solving that can be achieved through Model Eliciting Activities in preservice’s teacher of mathematics education of PTKIN in Aceh. This Research used qualitatif approach, since in this study want to describe the reality on the field namely data about the studets’ creativity in solving maths problem. The result showed that the componentsof flexibility obtained by contruction principle, the reality principle, and the self-just my assesment principle. Even so, there are student who got no flexibility with the third princ

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Verma, Abhishek, Anjana Chauhan, Ankit Pant, and Prerna Gupta. "Principle Component Analysis (PCA) of Wheat Genotypes Concerning Morphological Responses to Heat Stress." Journal of Advances in Biology & Biotechnology 28, no. 2 (2025): 443–53. https://doi.org/10.9734/jabb/2025/v28i22004.

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This study was conducted at Dev Bhoomi Uttarakhand University in Dehradun, India, with three replications using the Randomized Block Design (RBD) pattern to assess morphological responses to stress conditions in eight wheat genotypes (PBW-550, PBW-343, PBW-292, RAJ-3765, HD-2967, HD-3086, and NABI MG {BLACK WHEAT}. Wheat genotypes were subjected to Principle Component Analysis (PCA) during the investigation. The 1st principal component accounted for 47.2689% of the total variance (PC1). The second main component (PC2) contributed 24.6007% of the total variance. The 3rd main component was respo

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Honório, Bruno César Zanardo, Alexandre Cruz Sanchetta, Emilson Pereira Leite, and Alexandre Campane Vidal. "Independent component spectral analysis." Interpretation 2, no. 1 (2014): SA21—SA29. http://dx.doi.org/10.1190/int-2013-0074.1.

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Spectral decomposition techniques can break down the broadband seismic records into a series of frequency components that are useful for seismic interpretation and reservoir characterization. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency volume taking all the usable seismic bandwidth. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing the spectral components that best represent the whole seismic spectrum while keeping the main geological information. The ICA-based method g

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Feng, Yusi, Hongkai Chen, and Xin Zheng. "Component analysis of ancient glass products based on hierarchical analysis clustering algorithm." Highlights in Science, Engineering and Technology 21 (December 4, 2022): 180–85. http://dx.doi.org/10.54097/hset.v21i.3155.

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First, the attachment is pre-processed, and after the abnormal data are removed side by side, the bar chart is drawn to preliminarily analyze the relationship that lead-barium glass is easier to weathering than high potassium glass. Then the chi-square test is carried out to find that whether the weathering of the glass cultural relics surface is related to the glass type of the cultural relics, but there is no obvious relationship with the decoration and color of the cultural relics. Secondly, the statistical model of one-way ANOVA was established, and the difference analysis of various chemi

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Wang, Yuchen, Wenkai Lu, and Benfeng Wang. "An Events Rearrangement Strategy-Based Robust Principle Component Analysis." IEEE Geoscience and Remote Sensing Letters 14, no. 6 (2017): 881–85. http://dx.doi.org/10.1109/lgrs.2017.2685631.

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36

Murinto, Murinto. "PENGENALAN WAJAH MANUSIA DENGAN METODE PRINCIPLE COMPONENT ANALYSIS (PCA)." TELKOMNIKA (Telecommunication Computing Electronics and Control) 5, no. 3 (2007): 177. http://dx.doi.org/10.12928/telkomnika.v5i3.1364.

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Fiori, Simone. "Neural independent component analysis by ‘maximum-mismatch’ learning principle." Neural Networks 16, no. 8 (2003): 1201–21. http://dx.doi.org/10.1016/s0893-6080(03)00057-1.

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38

Sivasathya, M., and S. Mary Joans. "Image Feature Extraction using Non Linear Principle Component Analysis." Procedia Engineering 38 (2012): 911–17. http://dx.doi.org/10.1016/j.proeng.2012.06.114.

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39

Busel, Sergey Vladimirovich, and Ksenia Leonidovna Polupan. "The nature and features of the implementation of gamification as a systematic and specific game practice into the educational sphere." Samara Journal of Science 11, no. 4 (2022): 239–46. http://dx.doi.org/10.55355/snv2022114302.

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Based on the conducted content analysis of the definition of gamification in educational and non-educational contexts the author defines gamification. The main components of gamification are highlighted and described; they are game mechanics; game dynamics; game aesthetics, social interaction, and game elements. The principles of the implementation of gamification into the educational, organizational and management processes of an educational institution are articulated: the principle of instant feedback; the principle of convergence with interactive technologies; the principle of the fun comp

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Gai, Yu Chun, Wei Dong Zhu, Ying Lin Ke, Xiao Hong Yin, Jun Hao Wu, and Wei Wang. "Locating Error Analysis for Cartesian Positioner." Applied Mechanics and Materials 419 (October 2013): 157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.419.157.

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The structure and the operating principle of a Cartesian positioner are expounded. Based on the principle of multi-body system dynamics, the kinematic model of the Cartesian positioner is established. The deformation of each component is analyzed during the fuselage pose alignment by FEA(finite element analysis) . At last the locating error model is established. The locating error model can contribute to the stiffness distribution of components and the structure optimization of the Cartesian positioner.

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F. Mohamed, Ahmed, Fayez Wanis Zaki Soliman, and Mohamed Mahmoud Ashour. "Enhance watershed algorithms using principal component analysis capabilities." Al-Qadisiyah Journal for Engineering Sciences 17, no. 1 (2024): 5–15. http://dx.doi.org/10.30772/qjes.2024.145116.1055.

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Traditional image segmentation algorithms have many drawbacks, such as over-segmentation and image distortion due to reflected light. The Watershed algorithm is one of the most popular image segmentation algorithms. Over-segmentation errors caused by overlapping targets in the image, as well as noise and glare, must be removed. In this article, we apply image processing using the watershed algorithm and propose to improve the algorithm based on principal component analysis. PCA is a popular technique for analyzing large datasets with many advantages per observation. PCA improves data interpret

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Fitriani, Fitriani, Sudiyo Sudiyo, Dayang Berliana, and Evi Yuniarti. "Primary components in shaping koperasi masjid’s participation level: Employing a principle component analysis approach." Masyarakat, Kebudayaan dan Politik 31, no. 3 (2018): 318. http://dx.doi.org/10.20473/mkp.v31i32018.318-327.

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Koperasi masjid present themselves as a part of muamalah through Sharia economic facilitation. This study was conducted to analyse the cooperative members’ participation level in relation to the implementation of a cooperative business program. A survey using a case study approach was conducted at koperasi Masjid Hafshotul Iman, Masjid Taqwa and Masjid Darul HikmahThe survey employed a purposive sampling method with the participation of the cooperative’s management (chairman, secretary, and treasurer) and the masjid’s members. Ten respondents were purposively chosen from each mosque and a tota

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Bhavika, Dineshkumar Shah. "An application of Factor Analysis on GSRTC Data – A study case study of Ahmedabad Depot." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 07 (2018): 151–58. https://doi.org/10.5281/zenodo.1308129.

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This research paper is working to test an application of factor analysis and formation of model of factor analysis. Finally, it is tested for GSRTC data sets for the year 2005 to 2015 for selected factors affecting to the margin of GSRTC. The factors under study are taken for sixteen depot of Gujarat state. The concluded results are determined by taking an application of Bartlett’s test and principle component analysis.

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Liu, Guowei, Fengshan Ma, Gang Liu, Haijun Zhao, Jie Guo, and Jiayuan Cao. "Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China." Sustainability 11, no. 12 (2019): 3345. http://dx.doi.org/10.3390/su11123345.

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Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis iden

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Lokare, Chinmay, Rachana Patil, Saloni Rane, Deepakkumar Kathirasen, and Yogita Mistry. "Offline handwritten signature verification using various Machine Learning Algorithms." ITM Web of Conferences 40 (2021): 03010. http://dx.doi.org/10.1051/itmconf/20214003010.

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In today’s world it is necessary to protect one’s authenticity in order to ensure the protection of personal information that only the authenticate credentials of a person can have access to. Nowadays there is an increase in number of malpractices like signature forgery to access the important information of a person. To encounter signature verification problem, there have been a number of advances in verifying the authenticity of signature using various techniques including Machine Learning and Deep Learning. This paper introduces a novel approach to verify the signatures using difference of

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Maharnisha, Gandla, Gandla Roopesh Kumar, and R. Arunraj. "Satellite Image Registration and Image Fusion by using Principle Component Analysis." International Journal of Engineering & Technology 7, no. 2.19 (2018): 106. http://dx.doi.org/10.14419/ijet.v7i2.19.15063.

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This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired

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A. M., Mohamed,, Abdel Latif, S. H, and Alwan, A. S. "Support vector regression is an improvement for principle component analysis." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1699–715. http://dx.doi.org/10.51201/jusst/21/06462.

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The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis

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Wang, Wengang, Xu Hao, Yingchao Wang, and Yu Yuan. "Research on Rolling Bearing Operating Life Based on Principal Component Analysis." Journal of Physics: Conference Series 2468, no. 1 (2023): 012077. http://dx.doi.org/10.1088/1742-6596/2468/1/012077.

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Abstract In order to analyze the running state of rolling bearing, the principle component analysis is used to extract new parameters to analyze the running state of the rolling bearing. Firstly, the whole life cycle vibration signal of the rolling bearing is collected by using an acceleration sensor. Secondly, Empirical Mode Decomposition (EMD) is used to reduce the noise in the vibration signal. Then, the main components of the time domain parameters are extracted using the principle component of the denoised signal. And the main components are synthesized into new parameter indicators. Fina

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Hou Yuanyuan, 侯园园, and 徐建程 Xu Jiancheng. "Random Phase-Shifting Speckle Patterns Analysis Algorithm Based on Principle Component Analysis." Chinese Journal of Lasers 43, no. 12 (2016): 1204002. http://dx.doi.org/10.3788/cjl201643.1204002.

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Danklmayer, A., M. Chandra, and E. Lüneburg. "Principal Component Analysis In Radar Polarimetry." Advances in Radio Science 3 (May 13, 2005): 399–400. http://dx.doi.org/10.5194/ars-3-399-2005.

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Abstract:

Abstract. Second order moments of multivariate (often Gaussian) joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix). In Radar Polarimetry the application of the covariance matrix is known as target decomposition theory, which is a special application of the extremely versatile Principle Component Analysis (PCA). The basic idea of PCA is to convert a data set, consisting of correlated random variables into a new set of uncorrelated variables and order the new variables according to the value of th

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