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Constrained principal component anal...

Takane, Yoshio.

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Constrained principal component analysis and related techniques /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Constrained principal component analysis and related techniques // Yoshio Takane.
Author:	Takane, Yoshio.
Published:	Boca Raton :CRC Press, Taylor & Francis Group, : c2014.,
Description:	xvii, 233 p. :ill. ;25 cm.
Subject:	Principal components analysis. -
Online resource:	http://images.tandf.co.uk/common/jackets/websmall/978146655/9781466556669.jpg
ISBN:	9781466556669

Constrained principal component analysis and related techniques /
Takane, Yoshio.

Constrained principal component analysis and related techniques /Yoshio Takane. - Boca Raton :CRC Press, Taylor & Francis Group,c2014. - xvii, 233 p. :ill. ;25 cm. - Monographs on statistics and applied probability ;129.

Includes bibliographical references (p. 205-224) and index.

Introduction --

"In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mathematical ideas in CPCA: projection and singular value decomposition. The author then describes the basic data requirements, models, and analytical tools for CPCA and their immediate extensions. He also introduces techniques that are special cases of or closely related to CPCA and discusses several topics relevant to practical uses of CPCA. The book concludes with a technique that imposes different constraints on different dimensions (DCDD), along with its analytical extensions. MATLAB® programs for CPCA and DCDD as well as data to create the book's examples are available on the author's website"--

ISBN: 9781466556669UK59.99

LCCN: 2013039504Subjects--Topical Terms:

565921
Principal components analysis.

LC Class. No.: QA278.5 / .T35 2014

Dewey Class. No.: 519.5/35

Constrained principal component analysis and related techniques /
LDR:02434cam a2200241 a 4500 001 2017621
003 DLC
005 20140725103412.0
008 161003s2014 flua b 001 0 eng
010 $a 2013039504
020 $a 9781466556669 $q (hardback) : $c UK59.99
020 $a 1466556668 $q (hardback)
040 $a DLC $b eng $c DLC $d DLC
042 $a pcc
050 0 0 $a QA278.5 $b .T35 2014
082 0 0 $a 519.5/35 $2 23
100 1 $a Takane, Yoshio. $3 533229
245 1 0 $a Constrained principal component analysis and related techniques / $c Yoshio Takane.
260 $a Boca Raton : $b CRC Press, Taylor & Francis Group, $c c2014.
300 $a xvii, 233 p. : $b ill. ; $c 25 cm.
490 0 $a Monographs on statistics and applied probability ; $v 129
504 $a Includes bibliographical references (p. 205-224) and index.
505 0 $t Introduction -- $t Mathematical foundation -- $t Constrained principal component analysis (CPCA) -- $t Special cases and related methods -- $t Related topics of interest -- $t Different constraints on different dimensions (DCDD).
520 $a "In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mathematical ideas in CPCA: projection and singular value decomposition. The author then describes the basic data requirements, models, and analytical tools for CPCA and their immediate extensions. He also introduces techniques that are special cases of or closely related to CPCA and discusses several topics relevant to practical uses of CPCA. The book concludes with a technique that imposes different constraints on different dimensions (DCDD), along with its analytical extensions. MATLAB® programs for CPCA and DCDD as well as data to create the book's examples are available on the author's website"-- $c Provided by publisher.
650 0 $a Principal components analysis. $3 565921
650 0 $a Multivariate analysis. $3 517467
856 4 2 $3 Cover image $u http://images.tandf.co.uk/common/jackets/websmall/978146655/9781466556669.jpg