LEADER 01016nam a2200301 i 4500 001 991000844719707536 005 20020507174409.0 008 960827s1973 it ||| | ita 035 $ab10765268-39ule_inst 035 $aLE01303196$9ExL 040 $aDip.to Matematica$beng 082 0 $a001.6 084 $aAMS 68-01 084 $aAMS 68-XX 084 $aAMS 68P 100 1 $aBaroggi, Romeo$025513 245 10$aElaborazione e trasmissione dei dati a distanza :$btecniche e metodologie /$cRomeo Baroggi 250 $a2. ed 260 $aMilano :$bF. Angeli,$c1973 300 $a294 p. ;$c22 cm. 650 0$aComputer science 650 4$aTheory of data 907 $a.b10765268$b21-09-06$c28-06-02 912 $a991000844719707536 945 $aLE013 68-XX BAR11 (1973)$g1$i2013000056241$lle013$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i10860836$z28-06-02 996 $aElaborazione e trasmissione dei dati a distanza$9923451 997 $aUNISALENTO 998 $ale013$b01-01-96$cm$da $e-$fita$git $h0$i1 LEADER 04368nam 22006015 450 001 9910155526303321 005 20250408074044.0 010 $a981-10-0159-6 024 7 $a10.1007/978-981-10-0159-8 035 $a(CKB)4340000000027197 035 $a(DE-He213)978-981-10-0159-8 035 $a(MiAaPQ)EBC4767520 035 $a(PPN)197453007 035 $a(EXLCZ)994340000000027197 100 $a20161209d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonlinear Principal Component Analysis and Its Applications /$fby Yuichi Mori, Masahiro Kuroda, Naomichi Makino 205 $a1st ed. 2016. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2016. 215 $a1 online resource (VIII, 80 p. 17 illus., 8 illus. in color.) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$a981-10-0157-X 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $a1. Introduction -- 2. Nonlinear Principal Component Analysis -- 3. Application. 330 $aThis book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data.  In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology.  In the applications part of the book, four applications are introduced: variable selection for mixed measurement levels data, sparse MCA, joint dimension reduction and clustering methods for categorical data, and acceleration of ALS computation. The variable selection methods in PCA that originally were developed for numerical data can be applied to any types of measurement levels by using nonlinear PCA. Sparseness and joint dimension reduction and clustering for nonlinear data, the results of recent studies, are extensions obtained by the same matrix operations in nonlinear PCA. Finally, an acceleration algorithm is proposed to reduce the problem of computational cost in the ALS iteration in nonlinear multivariate methods.  This book thus presents the usefulness of nonlinear PCA which can be applied to different measurement levels data in diverse fields. As well, it covers the latest topics including the extension of the traditional statistical method, newly proposed nonlinear methods, and computational efficiency in the methods. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aStatistics 606 $aMathematical statistics$xData processing 606 $aSocial sciences$xStatistical methods 606 $aStatistical Theory and Methods 606 $aStatistics and Computing 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aStatistics. 615 0$aMathematical statistics$xData processing. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistical Theory and Methods. 615 24$aStatistics and Computing. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.5 700 $aMori$b Yuichi$4aut$4http://id.loc.gov/vocabulary/relators/aut$0756013 702 $aKuroda$b Masahiro$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMakino$b Naomichi$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910155526303321 996 $aNonlinear Principal Component Analysis and Its Applications$92218357 997 $aUNINA