LEADER 03320nam 2200613 450 001 9910789003903321 005 20230124192642.0 010 $a0-429-10809-5 010 $a1-4398-5729-6 035 $a(CKB)3710000000089971 035 $a(EBL)1480655 035 $a(SSID)ssj0001130988 035 $a(PQKBManifestationID)11614633 035 $a(PQKBTitleCode)TC0001130988 035 $a(PQKBWorkID)11110672 035 $a(PQKB)11709477 035 $a(MiAaPQ)EBC1480655 035 $a(EXLCZ)993710000000089971 100 $a20140313h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMultilinear subspace learning $edimensionality reduction of multidimensional data /$fHaiping Lu, K. N. Plataniotis, A. N. Venetsanopoulos 210 1$aBoca Raton, Florida :$cCRC Press,$d2014. 210 4$dİ2014 215 $a1 online resource (275 p.) 225 0 $aChapman & Hall/CRC machine learning & pattern recognition series Multilinear subspace learning 225 0$aChapman & Hall/CRC machine learning & pattern recognition series 300 $aDescription based upon print version of record. 311 $a1-4398-5724-5 320 $aIncludes bibliographical references. 327 $aFront Cover; Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data; Copyright; Dedication; Table of Contents; List of Figures; List of Tables; List of Algorithms; Acronyms and Symbols; Preface; 1. Introduction; Part I: Fundamentals and Foundations; 2. Linear Subspace Learning for Dimensionality Reduction; 3. Fundamentals of Multilinear Subspace Learning; 4. Overview of Multilinear Subspace Learning; 5. Algorithmic and Computational Aspects; Part II: Algorithms and Applications; 6. Multilinear Principal Component Analysis; 7. Multilinear Discriminant Analysis 327 $a8. Multilinear ICA, CCA, and PLS9. Applications of Multilinear Subspace Learning; Appendix A: Mathematical Background; Appendix B: Data and Preprocessing; Appendix C: Software; Bibliography; Back Cover 330 $aDue to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor.Multilinear Subspace Learning: Dimensionality Reduction of Mult 606 $aData compression (Computer science) 606 $aBig data 606 $aMultilinear algebra 615 0$aData compression (Computer science) 615 0$aBig data. 615 0$aMultilinear algebra. 676 $a005.7 686 $aCOM021030$aCOM037000$aTEC007000$2bisacsh 700 $aLu$b Haiping$01535310 701 $aPlataniotis$b K. N$01535311 701 $aVenetsanopoulos$b A. N$01535312 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910789003903321 996 $aMultilinear subspace learning$93783449 997 $aUNINA