MARC Record from marc_columbia

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001 15097405
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006 m o d
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008 140301t20142014flua ob 001 0 eng d
035 $a(OCoLC)ocn871223692
035 $a(NNC)15097405
040 $aEBLCP$beng$erda$epn$cEBLCP$dCUS$dWAU$dDEBSZ$dOCLCQ$dYDXCP$dOCLCF$dE7B$dUMI$dDEBBG$dOCLCQ$dMOR$dMERUC$dOCLCQ$dCEF$dOCLCQ$dWYU$dYDX$dOCLCQ$dVT2$dSFB$dK6U$dOCLCO$dOCLCQ
019 $a876044317$a899245816$a961544392$a1065944165$a1156386107$a1175722031
020 $a9781439857298
020 $a1439857296
020 $a1439857245
020 $a9781439857243
020 $a0429108095
020 $a9780429108099
020 $z9781439857243
035 $a(OCoLC)871223692$z(OCoLC)876044317$z(OCoLC)899245816$z(OCoLC)961544392$z(OCoLC)1065944165$z(OCoLC)1156386107$z(OCoLC)1175722031
037 $aCL0500000522$bSafari Books Online
050 4 $aQA76.9.D33 L825 2014
082 04 $a005.7
084 $aCOM021030$aCOM037000$aTEC007000$2bisacsh
049 $aZCUA
100 1 $aLu, Haiping,$eauthor.
245 10 $aMultilinear subspace learning :$bdimensionality reduction of multidimensional data /$cHaiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos.
264 1 $aBoca Raton, FL :$bCRC Press/Taylor and Francis Group,$c[2014]
264 4 $c©2014
300 $a1 online resource (xxvii, 268 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aChapman & Hall/CRC machine learning & pattern recognition series
504 $aIncludes bibliographical references and index.
505 0 $aIntroduction -- Fundamentals and Foundations -- Linear Subspace Learning for Dimensionality Reduction -- Fundamentals of Multilinear Subspace Learning -- Overview of Multilinear Subspace Learning -- Algorithmic and Computational Aspects -- Algorithms and Applications -- Multilinear Principal Component Analysis -- Multilinear Discriminant Analysis -- Multilinear ICA, CCA, and PLS -- Applications of Multilinear Subspace Learning -- Appendix A: Mathematical Background -- Appendix B: Data and Preprocessing -- Appendix C: Software.
520 $a"Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor. Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL. Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today's most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications. The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB source code, data, and other materials are available at www.comp.hkbu.edu.hk/~haiping/MSL.html"--$cProvided by publisher
588 0 $aPrint version record.
546 $aEnglish.
650 0 $aData compression (Computer science)
650 0 $aBig data.
650 0 $aMultilinear algebra.
650 6 $aDonnées$xCompression (Informatique)
650 6 $aDonnées volumineuses.
650 7 $aCOMPUTERS$xDatabase Management$xData Mining.$2bisacsh
650 7 $aCOMPUTERS$xMachine Theory.$2bisacsh
650 7 $aTECHNOLOGY & ENGINEERING$xElectrical.$2bisacsh
650 7 $aBig data.$2fast$0(OCoLC)fst01892965
650 7 $aData compression (Computer science)$2fast$0(OCoLC)fst00887919
650 7 $aMultilinear algebra.$2fast$0(OCoLC)fst01028903
700 1 $aPlataniotis, Konstantinos N.,$eauthor.
700 1 $aVenetsanopoulos, A. N.$q(Anastasios N.),$d1941-$eauthor.
776 08 $iPrint version:$aLu, Haiping.$tMultilinear subspace learning.$dBoca Raton : CRC Press, [2014]$z9781439857243$w(DLC) 2013039517$w(OCoLC)659750493
830 0 $aChapman & Hall/CRC machine learning & pattern recognition series.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15097405$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS