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Journal articles on the topic 'Principal components analysis'

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Published: 4 June 2021
Last updated: 30 July 2024

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1

Whitlark, David, and George H. Dunteman. "Principal Components Analysis." Journal of Marketing Research 27, no. 2 (May 1990): 243. http://dx.doi.org/10.2307/3172855.

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2

Kim, Sung-Hoon, and George H. Dunteman. "Principal Components Analysis." Journal of Educational Statistics 16, no. 2 (1991): 141. http://dx.doi.org/10.2307/1165117.

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3

Fujiwara, Masakazu, Tomohiro Minamidani, Isamu Nagai, and Hirofumi Wakaki. "Principal Components Regression by Using Generalized Principal Components Analysis." JOURNAL OF THE JAPAN STATISTICAL SOCIETY 43, no. 1 (2013): 57–78. http://dx.doi.org/10.14490/jjss.43.57.

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4

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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5

Voegtlin, Thomas. "Recursive principal components analysis." Neural Networks 18, no. 8 (October 2005): 1051–63. http://dx.doi.org/10.1016/j.neunet.2005.07.005.

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6

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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7

Yendle, Peter W., and Halliday J. H. MacFie. "Discriminant principal components analysis." Journal of Chemometrics 3, no. 4 (September 1989): 589–600. http://dx.doi.org/10.1002/cem.1180030407.

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8

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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9

Rutledge, Douglas N. "Comparison of Principal Components Analysis, Independent Components Analysis and Common Components Analysis." Journal of Analysis and Testing 2, no. 3 (July 2018): 235–48. http://dx.doi.org/10.1007/s41664-018-0065-5.

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10

Boudou, Alain, and Sylvie Viguier-Pla. "Principal components analysis and cyclostationarity." Journal of Multivariate Analysis 189 (May 2022): 104875. http://dx.doi.org/10.1016/j.jmva.2021.104875.

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11

Bahashwan, Ameerah O., Zakiah I. Kalantan, and Samia A. Adham. "Double gamma principal components analysis." Applied Mathematical Sciences 12, no. 11 (2018): 523–33. http://dx.doi.org/10.12988/ams.2018.8455.

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12

Whitlark, David. "Book Review: Principal Components Analysis." Journal of Marketing Research 27, no. 2 (May 1990): 243. http://dx.doi.org/10.1177/002224379002700216.

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13

Harris, Paul, Chris Brunsdon, and Martin Charlton. "Geographically weighted principal components analysis." International Journal of Geographical Information Science 25, no. 10 (October 2011): 1717–36. http://dx.doi.org/10.1080/13658816.2011.554838.

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14

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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15

Sainani, Kristin L. "Introduction to Principal Components Analysis." PM&R 6, no. 3 (February 22, 2014): 275–78. http://dx.doi.org/10.1016/j.pmrj.2014.02.001.

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16

CARDOT, HERVÉ. "Conditional Functional Principal Components Analysis." Scandinavian Journal of Statistics 34, no. 2 (June 2007): 317–35. http://dx.doi.org/10.1111/j.1467-9469.2006.00521.x.

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17

Fellus, Jerome, David Picard, and Philippe-Henri Gosselin. "Asynchronous gossip principal components analysis." Neurocomputing 169 (December 2015): 262–71. http://dx.doi.org/10.1016/j.neucom.2014.11.076.

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18

CRITCHLEY, FRANK. "Influence in principal components analysis." Biometrika 72, no. 3 (1985): 627–36. http://dx.doi.org/10.1093/biomet/72.3.627.

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19

McCown, William, Judith Johnson, and Thomas Petzel. "Procrastination, a principal components analysis." Personality and Individual Differences 10, no. 2 (January 1989): 197–202. http://dx.doi.org/10.1016/0191-8869(89)90204-3.

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20

Schneeweiss, H., and H. Mathes. "Factor Analysis and Principal Components." Journal of Multivariate Analysis 55, no. 1 (October 1995): 105–24. http://dx.doi.org/10.1006/jmva.1995.1069.

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21

Chapman, R. M., and J. W. Mccrary. "EP Component Identification and Measurement by Principal Components-Analysis." Brain and Cognition 27, no. 3 (April 1995): 288–310. http://dx.doi.org/10.1006/brcg.1995.1024.

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22

Chapman, Robert M., John W. McCrary, R. M. Chapman, and J. W. Mccrary. "EP Component Identification and Measurement by Principal Components-Analysis." Brain and Cognition 28, no. 3 (August 1995): 342. http://dx.doi.org/10.1006/brcg.1995.1262.

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23

Guo, Hao, Kurt J. Marfurt, and Jianlei Liu. "Principal component spectral analysis." GEOPHYSICS 74, no. 4 (July 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 spectra, with the importance of each spectrum being proportional to the size of its corresponding eigenvalue. Principal components are ideally suited to identify geologic features that give rise to anomalous moderate- to high-amplitude spectra. Unlike the input spectral magnitude and phase components, the principal component spectra are not direct indicators of bed thickness. By combining the variability of multiple components, principal component spectra highlight stratigraphic features that can be interpreted using a seismic geomorphology workflow. By mapping the three largest principal components using the three primary colors of red, green, and blue, we could represent more than 80% of the spectral variance with a single image. We have applied and validated this workflow using a broadband data volume containing channels draining an unconformity, which was acquired over the Central Basin Platform, Texas, U.S.A. Principal component analysis reveals a channel system with only a few output data volumes. The same process provides the interpreter with flexibility to remove any unwanted high-amplitude geologic trends or random noise from the original spectral components by eliminating those principal components that do not aid in delineation of prospective features with their interpretation during the reconstruction process.
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24

Tanaka, Yukata. "Sensitivity analysis in principal component analysis:influence on the subspace spanned by principal components." Communications in Statistics - Theory and Methods 17, no. 9 (January 1988): 3157–75. http://dx.doi.org/10.1080/03610928808829796.

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25

KARAKUZULU, Cihan, İbrahim Halil GÜMÜŞ, Serkan GÜLDAL, and Mustafa YAVAŞ. "Determining The Number of Principal Components with Schur's Theorem in Principal Component Analysis." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 2 (February 23, 2023): 299–306. http://dx.doi.org/10.17798/bitlisfen.1144360.

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Principal Component Analysis is a method for reducing the dimensionality of datasets while also limiting information loss. It accomplishes this by producing uncorrelated variables that maximize variance one after the other. The accepted criterion for evaluating a Principal Component’s (PC) performance is λ_j/tr(S) where tr(S) denotes the trace of the covariance matrix S. It is standard procedure to determine how many PCs should be maintained using a predetermined percentage of the total variance. In this study, the diagonal elements of the covariance matrix are used instead of the eigenvalues to determine how many PCs need to be considered to obtain the defined threshold of the total variance. For this, an approach which uses one of the important theorems of majorization theory is proposed. Based on the tests, this approach lowers the computational costs.
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26

Sundararajan, Raanju R. "Principal component analysis using frequency components of multivariate time series." Computational Statistics & Data Analysis 157 (May 2021): 107164. http://dx.doi.org/10.1016/j.csda.2020.107164.

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27

Mehlman, David W., Ursula L. Shepherd, and Douglas A. Kelt. "Bootstrapping Principal Components Analysis: A Comment." Ecology 76, no. 2 (March 1995): 640–43. http://dx.doi.org/10.2307/1941219.

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28

Prvan, T., and A. W. Bowman. "Nonparametric time dependent principal components analysis." ANZIAM Journal 44 (April 1, 2003): 627. http://dx.doi.org/10.21914/anziamj.v44i0.699.

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29

Kargi, H. "Principal components analysis for borate mapping." International Journal of Remote Sensing 28, no. 8 (April 2007): 1805–17. http://dx.doi.org/10.1080/01431160600905003.

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30

FEDERER, W. T., C. E. MCCULLOCH, and N. J. MILES-MCDERMOTT. "ILLUSTRATIVE EXAMPLES OF PRINCIPAL COMPONENTS ANALYSIS." Journal of Sensory Studies 2, no. 1 (March 1987): 37–54. http://dx.doi.org/10.1111/j.1745-459x.1987.tb00185.x.

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31

Wang, F. K., and James C. Chen. "CAPABILITY INDEX USING PRINCIPAL COMPONENTS ANALYSIS." Quality Engineering 11, no. 1 (September 1998): 21–27. http://dx.doi.org/10.1080/08982119808919208.

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32

Besse, Philippe, and J. O. Ramsay. "Principal components analysis of sampled functions." Psychometrika 51, no. 2 (June 1986): 285–311. http://dx.doi.org/10.1007/bf02293986.

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33

Shi, L. "Local influence in principal components analysis." Biometrika 84, no. 1 (March 1, 1997): 175–86. http://dx.doi.org/10.1093/biomet/84.1.175.

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34

Zhu, Mu. "Discriminant analysis with common principal components." Biometrika 93, no. 4 (December 1, 2006): 1018–24. http://dx.doi.org/10.1093/biomet/93.4.1018.

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35

LÓPEZ-RUBIO, EZEQUIEL, and JUAN MIGUEL ORTIZ-DE-LAZCANO-LOBATO. "DYNAMIC COMPETITIVE PROBABILISTIC PRINCIPAL COMPONENTS ANALYSIS." International Journal of Neural Systems 19, no. 02 (April 2009): 91–103. http://dx.doi.org/10.1142/s0129065709001860.

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We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.
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36

Zhang, Zhongheng, and Adela Castelló. "Principal components analysis in clinical studies." Annals of Translational Medicine 5, no. 17 (September 2017): 351. http://dx.doi.org/10.21037/atm.2017.07.12.

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37

Monmonier, Mark S. "A Spatially-Controlled Principal Components Analysis." Geographical Analysis 2, no. 2 (September 3, 2010): 192–95. http://dx.doi.org/10.1111/j.1538-4632.1970.tb00156.x.

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38

Ma, Jianzhong, and Christopher I. Amos. "Principal Components Analysis of Population Admixture." PLoS ONE 7, no. 7 (July 9, 2012): e40115. http://dx.doi.org/10.1371/journal.pone.0040115.

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39

Ekvall, Karl Oskar. "Targeted principal components regression." Journal of Multivariate Analysis 190 (July 2022): 104995. http://dx.doi.org/10.1016/j.jmva.2022.104995.

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40

Li, Zhaokai, Zihua Chai, Yuhang Guo, Wentao Ji, Mengqi Wang, Fazhan Shi, Ya Wang, Seth Lloyd, and Jiangfeng Du. "Resonant quantum principal component analysis." Science Advances 7, no. 34 (August 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 required to extract the principal components. In the experiment, we demonstrate the distillation of the first principal component of a 4 × 4 density matrix, with an efficiency of 86.0% and a fidelity of 0.90. This work shows the speedup ability of quantum algorithm in dimension reduction of data and thus could be used as part of quantum artificial intelligence algorithms in the future.
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41

Ferré, Louis. "Selection of components in principal component analysis: A comparison of methods." Computational Statistics & Data Analysis 19, no. 6 (June 1995): 669–82. http://dx.doi.org/10.1016/0167-9473(94)00020-j.

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42

Cadavid, A. C., J. K. Lawrence, and A. Ruzmaikin. "Principal Components and Independent Component Analysis of Solar and Space Data." Solar Physics 248, no. 2 (September 23, 2007): 247–61. http://dx.doi.org/10.1007/s11207-007-9026-2.

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43

Neal, Brent, and John C. Russ. "Principal Components Analysis of Multispectral Image Data." Microscopy Today 12, no. 5 (September 2004): 36–39. http://dx.doi.org/10.1017/s1551929500056297.

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Principal components analysis of multivariate data sets is a standard statistical method that was developed in the early halt or the 20th century. It provides researchers with a method for transforming their source data axes into a set of orthogonal principal axes and ranks. The rank for each axis in the principal set represents the significance of that axis as defined by the variance in the data along that axis. Thus, the first principal axis is the one with the greatest amount of scatter in the data and consequently the greatest amount of contrast and information, while the last principal axis represents the least amount of information.
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44

Sundberg, Per. "Shape and Size-Constrained Principal Components Analysis." Systematic Zoology 38, no. 2 (June 1989): 166. http://dx.doi.org/10.2307/2992385.

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45

Ellis, M. E. "Value at Risk Using Principal Components Analysis." CFA Digest 28, no. 2 (May 1998): 64–65. http://dx.doi.org/10.2469/dig.v28.n2.275.

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46

Kokoszka, Piotr, Stilian Stoev, and Qian Xiong. "Principal components analysis of regularly varying functions." Bernoulli 25, no. 4B (November 2019): 3864–82. http://dx.doi.org/10.3150/19-bej1113.

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47

Singh, Manoj K. "Value at Risk Using Principal Components Analysis." Journal of Portfolio Management 24, no. 1 (October 31, 1997): 101–12. http://dx.doi.org/10.3905/jpm.1997.409633.

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48

Hall, Peter, and Mohammad Hosseini-Nasab. "On properties of functional principal components analysis." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68, no. 1 (February 2006): 109–26. http://dx.doi.org/10.1111/j.1467-9868.2005.00535.x.

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49

Hosseinkashi, Yasaman, and Khalil Shafie. "Persian Handwriting Analysis Using Functional Principal Components." Journal of Statistical Research of Iran 6, no. 2 (March 1, 2010): 141–60. http://dx.doi.org/10.18869/acadpub.jsri.6.2.141.

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50

Real, Pedro L., James A. Moore, and James D. Newberry. "Principal components analysis of tree stem profiles." Canadian Journal of Forest Research 19, no. 12 (December 1, 1989): 1538–42. http://dx.doi.org/10.1139/x89-234.

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The use of principal components analysis to study tree stem profiles was critically analyzed during 1085 destructively sampled Douglas-fir trees and 1260 simulated trees with known geometric shapes. Interpretation about the meaning of each principal component is provided and contrasted with others in the forestry literature. Nearly identical results with both the destructively sampled and simulated trees, along with certain theoretical consideratons, indicate that the principal components are related to tree form as opposed to tree profile or taper. Therefore, principal components analysis is a useful analytical tool for stratifying trees into different form groups.
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