MARC Record from Library of Congress

LEADER: 03317cam a2200397 i 4500
001 2013039517
003 DLC
005 20140603080912.0
008 131104s2014 flua b 001 0 eng
010 $a 2013039517
020 $a9781439857243 (hardback)
040 $aDLC$beng$cDLC$erda$dDLC
042 $apcc
050 00 $aQA76.9.D33$bL825 2014
082 00 $a005.7$223
084 $aCOM021030$aCOM037000$aTEC007000$2bisacsh
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 $aBoca Raton, Florida :$bCRC Press/Taylor & Francis Group,$c[2014]
300 $axxvii, 268 pages :$billustrations ;$c25 cm.
336 $atext$2rdacontent
337 $aunmediated$2rdamedia
338 $avolume$2rdacarrier
490 0 $aChapman & Hall/CRC machine learning & pattern recognition series
500 $a"A Chapman & Hall Book"
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
504 $aIncludes bibliographical references (pages 231-261) and index.
650 0 $aData compression (Computer science)
650 0 $aBig data.
650 0 $aMultilinear algebra.
650 7 $aCOMPUTERS / Database Management / Data Mining.$2bisacsh
650 7 $aCOMPUTERS / Machine Theory.$2bisacsh
650 7 $aTECHNOLOGY & ENGINEERING / Electrical.$2bisacsh
700 1 $aPlataniotis, Konstantinos N.,$eauthor.
700 1 $aVenetsanopoulos, A. N.$q(Anastasios N.),$d1941-$eauthor.
856 42 $3Cover image$uhttp://images.tandf.co.uk/common/jackets/websmall/978143985/9781439857243.jpg