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010 a| 2013039517;

020 a| 9781439857243 (hardback);

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084 a| COM021030a| COM037000a| TEC0070002| bisacsh;

100 1 a| Lu, Haiping.?| UNAUTHORIZED;

245 10 a| Multilinear subspace learningh| [electronic resource] :b| dimensionality reduction of multidimensional data /c| Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos.;

260 a| Boca Raton, Florida :b| CRC Press/Taylor & Francis Group,c| [2014];

300 a| xxvii, 268 pages :b| illustrations ;c| 25 cm.;

490 0 a| Chapman & Hall/CRC machine learning & pattern recognition series;

500 a| "A Chapman & Hall Book";

504 a| Includes bibliographical references (pages 231-261) and index.;

506 a| Available only to authorized users.;

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"--c| Provided by publisher;

538 a| Mode of access: World Wide Web;

650 0 a| Data compression (Computer science)=| ^A185430;

650 0 a| Big data.=| ^A1157100;

650 0 a| Multilinear algebra.=| ^A183219;

650 7 a| COMPUTERS / Database Management / Data Mining.2| bisacsh;

650 7 a| COMPUTERS / Machine Theory.2| bisacsh;

650 7 a| TECHNOLOGY & ENGINEERING / Electrical.2| bisacsh;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Plataniotis, Konstantinos N.=| ^A1115048;

700 1 a| Venetsanopoulos, A. N.q| (Anastasios N.),d| 1941-=| ^A306453;

856 40 z| Full text available from Ebook Central - Academic Completeu| https://ebookcentral.proquest.com/lib/eastcarolina/detail.action?docID=1480655;

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Multilinear subspace learning dimensionality reduction of multidimensional data / Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos.

Author/creator	Lu, Haiping
Other author	Plataniotis, Konstantinos N.
Other author	Venetsanopoulos, A. N. (Anastasios N.), 1941-
Format	Electronic
Publication Info	Boca Raton, Florida : CRC Press/Taylor & Francis Group, [2014]
Description	xxvii, 268 pages : illustrations ; 25 cm.
Supplemental Content	Full text available from Ebook Central - Academic Complete
Subjects	Data compression (Computer science) Big data. Multilinear algebra.

Series	Chapman & Hall/CRC machine learning & pattern recognition series
Abstract	"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
General note	"A Chapman & Hall Book"
Bibliography note	Includes bibliographical references (pages 231-261) and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2013039517
ISBN	9781439857243 (hardback)

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Multilinear subspace learning dimensionality reduction of multidimensional data / Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos.

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