| 000 | 03566nam a2200469Ii 4500 | ||
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| 001 | CAH0KE12717PDF | ||
| 003 | FlBoTFG | ||
| 005 | 20171224124002.0 | ||
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| 007 | cr|||| | ||
| 008 | 150323t20142014flua ob 001 0 eng d | ||
| 020 | _a9781439857298 (e-book : PDF) | ||
| 040 |
_aFlBoTFG _beng _cFlBoTFG _erda |
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| 090 |
_aQA76.9.D33 _bL825 2013 |
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| 092 |
_a005.7 _bL926 |
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| 100 | 1 |
_aLu, Haiping, _eauthor. |
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| 245 | 1 | 0 |
_aMultilinear subspace learning : _bdimensionality reduction of multidimensional data / _cHaiping Lu, K.N. Plataniotis, A.N. Venetsanopoulos. |
| 264 | 1 |
_aBoca Raton : _bTaylor & Francis / CRC, _c[2014] |
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| 264 | 4 | _c�201 | |
| 300 |
_a1 online resource : _btext file, PD |
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| 336 |
_atext _2rdaconten |
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| 337 |
_acomputer _2rdamedi |
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| 338 |
_aonline resource _2rdacarrie |
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| 490 | _aChapman & Hall/CRC machine learning & pattern recognition serie | ||
| 504 | _aIncludes bibliographical references (pages 231-261) and index | ||
| 505 | _a1. Fundamentals and foundations -- 2. Algorithms and applications | ||
| 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 |
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| 530 | _aAlso available in print format | ||
| 650 | _aData compression (Computer science | ||
| 650 | _aBig data | ||
| 650 | _aMultilinear algebra | ||
| 655 |
_aElectronic books. _2lcs |
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| 700 |
_aPlataniotis, Konstantinos N., _eauthor |
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| 700 |
_aVenetsanopoulos, A. N. _q(Anastasios N.), _d1941- _eauthor |
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| 776 |
_iPrint version: _z978143985724 |
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| 830 | _aChapman & Hall/CRC machine learning & pattern recognition series | ||
| 856 |
_uhttp://marc.crcnetbase.com/isbn/9781439857298 _qapplication/PDF _zDistributed by publisher. Purchase or institutional license may be required for access |
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| 913 | _112594 | ||
| 993 | _aCAH0KE12717PD | ||
| 998 | _aexisting prin | ||
| 998 |
_axxvii, 268 pages : _billustrations ; _c[ca. 23-29] cm |
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| 999 |
_a978143985724 _c16157 _d16157 |
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