03401nam a2200421Ii 4500 CAH0KE12717PDF FlBoTFG 20171224123808.0 m|||||o||d|||||||| cr|||| 150323t20142014flua ob 001 0 eng d 9781439857298 (ebook : PDF) FlBoTFG eng FlBoTFG rda QA76.9.D33 L825 2013 005.7 L926 Lu, Haiping, author. Multilinear subspace learning : dimensionality reduction of multidimensional data / Haiping Lu, K.N. Plataniotis, A.N. Venetsanopoulos. Boca Raton : Taylor & Francis / CRC, [2014] �201 1 online resource : text file, PD text rdaconten computer rdamedi online resource rdacarrie Chapman & Hall/CRC machine learning & pattern recognition serie Includes bibliographical references (pages 231-261) and index 1. Fundamentals and foundations -- 2. Algorithms and applications "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"-- Provided by publisher Also available in print format Data compression (Computer science Big data Multilinear algebra Electronic books. lcs Plataniotis, Konstantinos N., author Venetsanopoulos, A. N. (Anastasios N.), 1941- author Print version: 978143985724 Chapman & Hall/CRC machine learning & pattern recognition series http://marc.crcnetbase.com/isbn/9781439857298 application/PDF Distributed by publisher. Purchase or institutional license may be required for access 15764 15764