LEADER 03926oam 2200673I 450 001 9910954383703321 005 20170816135619.0 010 $a9781040079454 010 $a1040079458 010 $a9780429188374 010 $a0429188374 010 $a9781466556669 010 $a1466556668 024 7 $a10.1201/b16020 035 $a(CKB)2670000000394990 035 $a(EBL)1402688 035 $a(SSID)ssj0001040307 035 $a(PQKBManifestationID)11592788 035 $a(PQKBTitleCode)TC0001040307 035 $a(PQKBWorkID)11001677 035 $a(PQKB)10307777 035 $a(MiAaPQ)EBC1402688 035 $a(OCoLC)863136068 035 $a(OCoLC)1190651007 035 $a(OCoLC)on1190651007 035 $a(CaSebORM)9781466556683 035 $a(EXLCZ)992670000000394990 100 $a20180331d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aConstrained principal component analysis and related techniques /$fYoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada 205 $a1st edition 210 1$aBoca Raton :$cChapman and Hall/CRC,$d2014. 210 4$dİ2014 215 $a1 online resource (244 p.) 225 1 $aMonographs on statistics and applied probability ;$v129 300 $aDescription based upon print version of record. 311 08$a9781466556683 311 08$a1466556684 320 $aIncludes bibliographical references. 327 $aFront Cover; Contents; List of Figures; List of Tables; Preface; About the Author; Chapter 1 Introduction; Chapter 2 Mathematical Foundation; Chapter 3 Constrained Principal Component Analysis (CPCA); Chapter 4 Special Cases and Related Methods; Chapter 5 Related Topics of Interest; Chapter 6 Different Constraints on Different Dimensions (DCDD); Epilogue; Appendix; Bibliography; Back Cover 330 $aIn multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concrete examples of CPCA that provide readers with a basic understanding of the technique and its applications. It gives a detailed account of two key mathematical ideas in CPCA: projection and singular value decomposition. The author then describes the basic data requirements, models, and analytical tools for CPCA and their immediate extensions. He also introduces techniques that are special cases of or closely related to CPCA and discusses several topics relevant to practical uses of CPCA. The book concludes with a technique that imposes different constraints on different dimensions (DCDD), along with its analytical extensions. MATLAB programs for CPCA and DCDD as well as data to create the book's examples are available on the author's website--$cProvided by publisher. 410 0$aMonographs on statistics and applied probability (Series) ;$v129. 606 $aPrincipal components analysis 606 $aMultivariate analysis 615 0$aPrincipal components analysis. 615 0$aMultivariate analysis. 676 $a519.5/35 686 $aMAT029000$2bisacsh 700 $aTakane$b Yoshio$01791322 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910954383703321 996 $aConstrained principal component analysis and related techniques$94328585 997 $aUNINA