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Otie Kilmer and Rosemary Kilmer 205 $aThird edition. 210 1$aHoboken, New Jersey :$cWiley,$d2016. 210 4$d2016 215 $a1 online resource 300 $aIncludes index. 606 $aBuilding$xDetails$vDrawings 606 $aInterior architecture 606 $aStructural drawing 615 0$aBuilding$xDetails 615 0$aInterior architecture. 615 0$aStructural drawing. 676 $a729.022 700 $aKilmer$b W. 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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