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Constrained principal component anal...

Takane, Yoshio.

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, 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 (hbk.) :

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

Constrained principal component analysis and related techniques /Yoshio Takane. - Boca Raton :CRC, 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: 9781466556669 (hbk.) :NT$2597

LCCN: 2013039504Subjects--Topical Terms:

182575
Principal components analysis.

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

Dewey Class. No.: 519.5/35

Constrained principal component analysis and related techniques /
LDR:02488cam a2200265 a 4500 001 427295
003 DLC
005 20140725103412.0
008 140917s2014 flua b 001 0 eng
010 $a 2013039504
020 $a 9781466556669 (hbk.) : $c NT$2597
020 $a 1466556668 (hbk.)
035 $a 2013039504
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
084 $a MAT029000 $2 bisacsh
100 1 $a Takane, Yoshio. $3 218598
245 1 0 $a Constrained principal component analysis and related techniques / $c Yoshio Takane.
260 $a Boca Raton : $b CRC, 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 182575
650 0 $a Multivariate analysis. $3 181905
650 7 $a MATHEMATICS / Probability & Statistics / General. $2 bisacsh $3 603543
856 4 2 $3 Cover image $u http://images.tandf.co.uk/common/jackets/websmall/978146655/9781466556669.jpg