LEADER 03273oam 2200721I 450 001 9910453732903321 005 20200520144314.0 010 $a0-429-14820-8 010 $a1-4398-0616-0 024 7 $a10.1201/b16017 035 $a(CKB)2550000001167919 035 $a(EBL)1433364 035 $a(OCoLC)865330387 035 $a(SSID)ssj0001175466 035 $a(PQKBManifestationID)11761109 035 $a(PQKBTitleCode)TC0001175466 035 $a(PQKBWorkID)11121865 035 $a(PQKB)10137816 035 $a(MiAaPQ)EBC1433364 035 $a(CaSebORM)9781439806166 035 $a(Au-PeEL)EBL1433364 035 $a(CaPaEBR)ebr10811767 035 $a(CaONFJC)MIL549252 035 $a(OCoLC)879631718 035 $a(OCoLC)863715573 035 $a(EXLCZ)992550000001167919 100 $a20180331h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMulti-label dimensionality reduction /$fLiang Sun, Shuiwang Ji, and Jieping Ye 205 $a1st edition 210 1$aBoca Raton, FL :$cCRC Press,$d[2014] 210 4$dİ2014 215 $a1 online resource (206 p.) 225 1 $aChapman & Hall/CRC machine learning & pattern recognition series 225 0$aChapman & Hall/CRC machine learning & pattern recognition series 300 $aDescription based upon print version of record. 311 $a1-4398-0615-2 311 $a1-306-18001-5 320 $aIncludes bibliographical references. 327 $aCover; Series; Contents; Preface; Symbol Description; Chapter 1: Introduction; Chapter 2: Partial Least Squares; Chapter 3: Canonical Correlation Analysis; Chapter 4: Hypergraph Spectral Learning; Chapter 5: A Scalable Two-Stage Approach for Dimensionality Reduction; Chapter 6: A Shared-Subspace Learning Framework; Chapter 7: Joint Dimensionality Reduction and Classification; Chapter 8: Nonlinear Dimensionality Reduction: Algorithms and Applications; Appendix Proofs; References; Back Cover 330 $aSimilar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks a unified treatment of multi-label dimensionality reduction that incorporates both algorithmic developments and applications. Addressing this shortfall, Multi-Label Dimensionality Reduction covers the methodological developments, theoretical properti 410 0$aChapman & Hall/CRC machine learning & pattern recognition series. 606 $aComputational complexity 606 $aMachine learning 606 $aPattern perception 608 $aElectronic books. 615 0$aComputational complexity. 615 0$aMachine learning. 615 0$aPattern perception. 676 $a006.3/1 700 $aSun$b Liang$0940395 702 $aJi$b Shuiwang 702 $aYe$b Jieping 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910453732903321 996 $aMulti-label dimensionality reduction$92120703 997 $aUNINA