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Multilinear subspace learning : dimensionality reduction of multidimensional data

Author: Haiping Lu; Konstantinos N Plataniotis; A N Venetsanopoulos
Publisher: Boca Raton : Taylor & Francis / CRC, [2014] ©2014
Series: Chapman & Hall/CRC machine learning & pattern recognition series.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"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)  Read more...
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Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Haiping Lu; Konstantinos N Plataniotis; A N Venetsanopoulos
ISBN: 9781439857298 1439857296 9781439857243 1439857245
OCLC Number: 880788144
Description: 1 online resource : text file, PDF.
Contents: 1. Fundamentals and foundations --
2. Algorithms and applications.
Series Title: Chapman & Hall/CRC machine learning & pattern recognition series.
Responsibility: Haiping Lu, K.N. Plataniotis, A.N. Venetsanopoulos.

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"--

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"...this book is built to be read as a rich and yet accessible introduction... artfully structured for a specialized audience of new researchers and bleeding-edge practitioners. ...The treatment Read more...

 
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