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Books on the topic 'Shape principal component analysis'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

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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2

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

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3

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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4

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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5

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

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6

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

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7

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

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8

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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9

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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10

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

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11

Tanaka-Yamawaki, Mieko, and Yumihiko Ikura. Principal Component Analysis and Randomness Test for Big Data Analysis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3967-9.

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12

Binongo, José Nilo G. Stylometry and its implementation by principal component analysis. The Author], 2000.

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13

Rijckevorsel, Jan L. A. van. and Leeuw Jan de, eds. Component and correspondence analysis: Dimension reductionby functional approximation. Wiley, 1988.

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14

Niesing, Jan. Simultaneous component and factor analysis methods for two or more groups: A comparative study. DSWO Press, Leiden University, 1997.

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15

Rijckevorsel, Jan L. A. van. and Leeuw Jan de, eds. Component and correspondence analysis: Dimension reduction by functional approximation. Wiley, 1988.

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16

Checchi, Daniele. Economic interdependence and structural change: Some results from principal component analysis. Dept. of Economics and Government, Newcastle upon Tyne Polytechnic, 1989.

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17

Archibald, T. W. Application of principal component analysis to an aggregate stochastic dynamic programming model of multiple reservoir systems. University of Edinburgh, Management School, 1995.

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18

Eder, B. K. The spatial and temporal analysis of non-urban ozone concentrations over the eastern United States using rotated principal component analysis. U.S. Environmental Protection Agency, 1992.

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19

Eder, B. K. The spatial and temporal analysis of non-urban ozone concentrations over the eastern United States using rotated principal component analysis. U.S. Environmental Protection Agency, 1992.

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20

Zhang, Wenfei. Regression based principal component analysis for sparse functional data with applications to screening pubertal growth paths. [publisher not identified], 2012.

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21

Akiode, Mofoluke. Factor Analysis Using the Principal Component (PCA) Method in SPSS With Data From CGAP Smallholder Household Survey (2016). SAGE Publications Ltd, 2025. https://doi.org/10.4135/9781036216504.

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22

Rovida, Maria Antonietta, ed. Fonti per la storia dell'architettura, della città, del territorio. Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-722-5.

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The contributions presented at the study day held in Empoli in May 2006 – now collected in book form – are intended to provide a contribution to the debate on the relations between the teaching of history of architecture, design and historiography. Each essay addresses a specific issue, proposing an analysis and valorisation of the sources (documents, images, diaries etc.) and the resources available for research, representation and design. Taken as a whole, the collective work aims at defining a history of architecture focused on a knowledge and understanding of how, at different times and in

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23

Sanguansat, Parinya, ed. Principal Component Analysis. InTech, 2012. http://dx.doi.org/10.5772/2340.

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24

Principal Component Analysis. University of Illinois, 2016. http://dx.doi.org/10.4135/9781529773163.

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25

Principal Component Analysis. Springer-Verlag, 2002. http://dx.doi.org/10.1007/b98835.

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26

Principal Component Analysis. Springer London, Limited, 2006.

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27

Principal component analysis. Springer-Verlag, 1986.

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28

Principal component analysis. 2nd ed. Springer, 2002.

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29

Principal Component Analysis. Springer London, Limited, 2013.

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30

Principal Component Analysis. IntechOpen, 2012.

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31

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer, 2018.

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32

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer, 2016.

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33

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer London, Limited, 2016.

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34

Sastry, Shankar, Yi Ma, and René Vidal. Generalized Principal Component Analysis. Springer, 2016.

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35

Oja, Erkki, Aapo Hyvärinen, and Juha Karhunen. Independent Component Analysis. Wiley-Interscience, 2001.

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36

Principal Component Analysis [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.97992.

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37

Sanguansat, Parinya, ed. Principal Component Analysis - Engineering Applications. InTech, 2012. http://dx.doi.org/10.5772/2693.

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38

Sanguansat, Parinya, ed. Principal Component Analysis - Multidisciplinary Applications. InTech, 2012. http://dx.doi.org/10.5772/2694.

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39

Márquez, Fausto Pedro García. Advances in Principal Component Analysis. IntechOpen, 2022.

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40

Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how

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41

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2016.

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42

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2017.

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43

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2018.

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44

Kong, Xiangyu, Changhua Hu, and Zhansheng Duan. Principal Component Analysis Networks and Algorithms. Springer, 2017.

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45

Principal Component Analysis: Methods, Applications and Technology. Nova Science Publishers, Incorporated, 2017.

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46

Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. Nonlinear Principal Component Analysis and Its Applications. Springer London, Limited, 2016.

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47

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2013.

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48

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2020.

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49

Takane, Yoshio. Constrained Principal Component Analysis and Related Techniques. Taylor & Francis Group, 2016.

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50

Mori, Yuichi, Masahiro Kuroda, and Naomichi Makino. Nonlinear Principal Component Analysis and Its Applications. Springer, 2016.

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