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Journal articles on the topic 'Principal component analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Barros, António S., and Douglas N. Rutledge. "Segmented principal component transform–principal component analysis." Chemometrics and Intelligent Laboratory Systems 78, no. 1-2 (2005): 125–37. http://dx.doi.org/10.1016/j.chemolab.2005.01.003.

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2

Gewers, Felipe L., Gustavo R. Ferreira, Henrique F. De Arruda, et al. "Principal Component Analysis." ACM Computing Surveys 54, no. 4 (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

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3

Richards, Larry E., and I. T. Jolliffe. "Principal Component Analysis." Journal of Marketing Research 25, no. 4 (1988): 410. http://dx.doi.org/10.2307/3172953.

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4

Lever, Jake, Martin Krzywinski, and Naomi Altman. "Principal component analysis." Nature Methods 14, no. 7 (2017): 641–42. http://dx.doi.org/10.1038/nmeth.4346.

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5

Timmerman, Marieke E. "Principal Component Analysis." Journal of the American Statistical Association 98, no. 464 (2003): 1082–83. http://dx.doi.org/10.1198/jasa.2003.s308.

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6

Goodall, Colin. "Principal Component Analysis." Technometrics 30, no. 3 (1988): 351–52. http://dx.doi.org/10.1080/00401706.1988.10488412.

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7

Wold, Svante, Kim Esbensen, and Paul Geladi. "Principal component analysis." Chemometrics and Intelligent Laboratory Systems 2, no. 1-3 (1987): 37–52. http://dx.doi.org/10.1016/0169-7439(87)80084-9.

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8

Law, John, and I. T. Jolliffe. "Principal Component Analysis." Statistician 36, no. 4 (1987): 432. http://dx.doi.org/10.2307/2348864.

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9

Hess, Aaron S., and John R. Hess. "Principal component analysis." Transfusion 58, no. 7 (2018): 1580–82. http://dx.doi.org/10.1111/trf.14639.

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10

Bro, Rasmus, and Age K. Smilde. "Principal component analysis." Anal. Methods 6, no. 9 (2014): 2812–31. http://dx.doi.org/10.1039/c3ay41907j.

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11

Abdi, Hervé, and Lynne J. Williams. "Principal component analysis." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 4 (2010): 433–59. http://dx.doi.org/10.1002/wics.101.

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12

Guo, Hao, Kurt J. Marfurt, and Jianlei Liu. "Principal component spectral analysis." GEOPHYSICS 74, no. 4 (2009): P35—P43. http://dx.doi.org/10.1190/1.3119264.

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Spectral decomposition methods help illuminate lateral changes in porosity and thin-bed thickness. For broadband data, an interpreter might generate 80 or more somewhat redundant amplitude and phase spectral components spanning the usable seismic bandwidth at [Formula: see text] intervals. Large numbers of components can overload not only the interpreter but also the display hardware. We have used principal component analysis to reduce the multiplicity of spectral data and enhance the most energetic trends inside the data. Each principal component spectrum is mathematically orthogonal to other

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13

Jiang, Hansi. "Modularity Component Analysis versus Principal Component Analysis." American Journal of Applied Mathematics 4, no. 2 (2016): 99. http://dx.doi.org/10.11648/j.ajam.20160402.15.

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14

O., P. Sheoran, Kumar Vinay, Poonia Hemant, and Malik Komal. "Principal Component Analysis - Online Statistical Analysis Tool." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3050–54. https://doi.org/10.35940/ijeat.C6014.029320.

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An online module to deal with PCA has been developed in ASP scripting language based on Server-Client Architecture. The module produces descriptive statistics via subprogram Descriptive Stats, computes eigenvalues and eigenvector using MxEigen Jacobisub-program, order eigenvector through MxEigsrtsub-program and finally produces eigenvalues, eigenvectors, output loadings and components scores through Output Eigenval, Output Loadings, Output Scoressub-programs. A user friendly interface has been developed for entering or pasting the data, entering various parameters such as number of variables,

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15

Saegusa, Ryo, Hitoshi Sakano, and Shuji Hashimoto. "Nonlinear principal component analysis to preserve the order of principal components." Neurocomputing 61 (October 2004): 57–70. http://dx.doi.org/10.1016/j.neucom.2004.03.004.

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16

Moller, R., and A. Konies. "Coupled Principal Component Analysis." IEEE Transactions on Neural Networks 15, no. 1 (2004): 214–22. http://dx.doi.org/10.1109/tnn.2003.820439.

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17

Hu, Yu-Pin, and Ruey S. Tsay. "Principal Volatility Component Analysis." Journal of Business & Economic Statistics 32, no. 2 (2014): 153–64. http://dx.doi.org/10.1080/07350015.2013.818006.

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18

Tipping, Michael E., and Christopher M. Bishop. "Probabilistic Principal Component Analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61, no. 3 (1999): 611–22. http://dx.doi.org/10.1111/1467-9868.00196.

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19

Zou, Hui, Trevor Hastie, and Robert Tibshirani. "Sparse Principal Component Analysis." Journal of Computational and Graphical Statistics 15, no. 2 (2006): 265–86. http://dx.doi.org/10.1198/106186006x113430.

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20

Lloyd, Seth, Masoud Mohseni, and Patrick Rebentrost. "Quantum principal component analysis." Nature Physics 10, no. 9 (2014): 631–33. http://dx.doi.org/10.1038/nphys3029.

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21

Kao, Yi-Hao, and Benjamin Van Roy. "Directed Principal Component Analysis." Operations Research 62, no. 4 (2014): 957–72. http://dx.doi.org/10.1287/opre.2014.1290.

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22

Akinduko, A. A., and A. N. Gorban. "Multiscale principal component analysis." Journal of Physics: Conference Series 490 (March 11, 2014): 012081. http://dx.doi.org/10.1088/1742-6596/490/1/012081.

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23

Jie Luo, Bo Hu, Xie-Ting Ling, and Ruey-Wen Liu. "Principal independent component analysis." IEEE Transactions on Neural Networks 10, no. 4 (1999): 912–17. http://dx.doi.org/10.1109/72.774259.

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24

Wiesel, A., and A. O. Hero. "Decomposable Principal Component Analysis." IEEE Transactions on Signal Processing 57, no. 11 (2009): 4369–77. http://dx.doi.org/10.1109/tsp.2009.2025806.

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25

Gupta, Ajay, and Adrian Barbu. "Parameterized principal component analysis." Pattern Recognition 78 (June 2018): 215–27. http://dx.doi.org/10.1016/j.patcog.2018.01.018.

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26

Li, TianJiang, and Qiang Du. "Abstract principal component analysis." Science China Mathematics 56, no. 12 (2013): 2783–98. http://dx.doi.org/10.1007/s11425-013-4715-9.

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27

Candès, Emmanuel J., Xiaodong Li, Yi Ma, and John Wright. "Robust principal component analysis?" Journal of the ACM 58, no. 3 (2011): 1–37. http://dx.doi.org/10.1145/1970392.1970395.

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28

Liwicki, Stephan, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic. "Euler Principal Component Analysis." International Journal of Computer Vision 101, no. 3 (2012): 498–518. http://dx.doi.org/10.1007/s11263-012-0558-z.

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29

Fearn, Tom. "Probabilistic Principal Component Analysis." NIR news 25, no. 3 (2014): 23. http://dx.doi.org/10.1255/nirn.1439.

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30

Sando, Keishi, and Hideitsu Hino. "Modal Principal Component Analysis." Neural Computation 32, no. 10 (2020): 1901–35. http://dx.doi.org/10.1162/neco_a_01308.

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Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and various robust PCA methods have been proposed. It has been shown that the robustness of many statistical methods can be improved using mode estimation instead of mean estimation, because mode estimation is not significantly affected by the presence of outliers. Thus, this study proposes a modal principal component analysis (MPCA), which is a robust PCA method based on mode estimation. The proposed method finds

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31

Nounou, Mohamed N., Bhavik R. Bakshi, Prem K. Goel, and Xiaotong Shen. "Bayesian principal component analysis." Journal of Chemometrics 16, no. 11 (2002): 576–95. http://dx.doi.org/10.1002/cem.759.

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32

Aflalo, Yonathan, and Ron Kimmel. "Regularized principal component analysis." Chinese Annals of Mathematics, Series B 38, no. 1 (2017): 1–12. http://dx.doi.org/10.1007/s11401-016-1061-6.

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33

Gniazdowski, Zenon. "Principal Component Analysis versus Factor Analysis." Zeszyty Naukowe WWSI 15, no. 24 (2021): 35——88. https://doi.org/10.26348/znwwsi.24.35.

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The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors in FA was discussed in detail. A new criterion for determining the number of factors and principal components is discussed, which will allow to present most of the variance of each of the analyzed primary variables. An efficient algorithm for determining the number of factors in F

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34

Hassani, Sahar, Harald Martens, El Mostafa Qannari, and Achim Kohler. "Degrees of freedom estimation in Principal Component Analysis and Consensus Principal Component Analysis." Chemometrics and Intelligent Laboratory Systems 118 (August 2012): 246–59. http://dx.doi.org/10.1016/j.chemolab.2012.05.015.

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35

Li, Zhaokai, Zihua Chai, Yuhang Guo, et al. "Resonant quantum principal component analysis." Science Advances 7, no. 34 (2021): eabg2589. http://dx.doi.org/10.1126/sciadv.abg2589.

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Principal component analysis (PCA) has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the principal components of it, i.e., the eigenvectors of the density matrix with the largest eigenvalues. However, because of the substantial resource requirement, its experimental implementation remains challenging. Here, we develop a resonant analysis algorithm with minimal resource for ancillary qubits, in which only one frequency-scanning probe qubit is r

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36

Abegaz, Fentaw, Kridsadakorn Chaichoompu, Emmanuelle Génin, et al. "Principals about principal components in statistical genetics." Briefings in Bioinformatics 20, no. 6 (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,

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37

prakash, Mr S. Om, and Gokila S. "Principal Component Analysis - A Survey." IJARCCE 7, no. 8 (2018): 63–66. http://dx.doi.org/10.17148/ijarcce.2018.7814.

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38

McLean, Robert A. "Immunization Using Principal Component Analysis." CFA Digest 27, no. 3 (1997): 32–33. http://dx.doi.org/10.2469/dig.v27.n3.112.

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39

Chun, Young-Jae, Kyoung-Su Oh, and Sung-Hyun Cho. "Photomosaics Using Principal Component Analysis." Journal of Korea Game Society 11, no. 1 (2011): 139–46. http://dx.doi.org/10.7583/jkgs.2011.11.1.139.

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40

Adamu, Nuraddeen, Samaila Abdullahi, and Sani Musa. "Online Stochastic Principal Component Analysis." Caliphate Journal of Science and Technology 4, no. 1 (2022): 101–8. http://dx.doi.org/10.4314/cajost.v4i1.13.

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This paper studied Principal Component Analysis (PCA) in an online. The problem is posed as a subspace optimization problem and solved using gradient based algorithms. One such algorithm is the Variance-Reduced PCA (VR-PCA). The VR-PCA was designed as an improvement to the classical online PCA algorithm known as the Oja’s method where it only handled one sample at a time. The paper developed Block VR-PCA as an improved version of VR-PCA. Unlike prominent VR-PCA, the Block VR-PCA was designed to handle more than one dimension in subspace optimization at a time and it showed good performance. Th

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41

Everitt, B. S., and P. M. Kroonenberg. "Three-Mode Principal Component Analysis." Biometrics 42, no. 1 (1986): 224. http://dx.doi.org/10.2307/2531268.

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42

Barber, Joel R., and Mark L. Copper. "Immunization Using Principal Component Analysis." Journal of Portfolio Management 23, no. 1 (1996): 99–105. http://dx.doi.org/10.3905/jpm.1996.409574.

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43

TAKANE, Yoshio. "ON CONSTRAINED PRINCIPAL COMPONENT ANALYSIS." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 19, no. 1 (1992): 29–39. http://dx.doi.org/10.2333/jbhmk.19.29.

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44

Gortler, Jochen, Thilo Spinner, Dirk Streeb, Daniel Weiskopf, and Oliver Deussen. "Uncertainty-Aware Principal Component Analysis." IEEE Transactions on Visualization and Computer Graphics 26, no. 1 (2020): 822–31. http://dx.doi.org/10.1109/tvcg.2019.2934812.

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45

Tharwat, Alaa. "Principal component analysis - a tutorial." International Journal of Applied Pattern Recognition 3, no. 3 (2016): 197. http://dx.doi.org/10.1504/ijapr.2016.079733.

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46

Tharwat, Alaa. "Principal component analysis - a tutorial." International Journal of Applied Pattern Recognition 3, no. 3 (2016): 197. http://dx.doi.org/10.1504/ijapr.2016.10000630.

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47

Vidal, R., Yi Ma, and S. Sastry. "Generalized principal component analysis (GPCA)." IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 12 (2005): 1945–59. http://dx.doi.org/10.1109/tpami.2005.244.

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48

Shou, Haochang, Vadim Zipunnikov, Ciprian M. Crainiceanu, and Sonja Greven. "Structured functional principal component analysis." Biometrics 71, no. 1 (2014): 247–57. http://dx.doi.org/10.1111/biom.12236.

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49

Lin, Zhenhua, Liangliang Wang, and Jiguo Cao. "Interpretable functional principal component analysis." Biometrics 72, no. 3 (2015): 846–54. http://dx.doi.org/10.1111/biom.12457.

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50

Sang, Peijun, Liangliang Wang, and Jiguo Cao. "Parametric functional principal component analysis." Biometrics 73, no. 3 (2017): 802–10. http://dx.doi.org/10.1111/biom.12641.

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