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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/Crc Machine Learning & Pattern Recognition) 1st Edition

ISBN-13: 978-1439857243
ISBN-10: 1439857245
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Editorial Reviews

Review

"…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 builds an overarching framework and provides an analytical reader with a well-expressed taxonomy on the foundations of historical developments and similarity in content and goals. Thus, packaged, current research is endowed with instant meaning and purpose, the derivation of which would initially elude a newcomer to this complex and articulated branch of machine learning."
―Computing Reviews, November 2014

"Experimentally inclined readers will probably like this book … . Practitioners will appreciate that the presentation of the subject matter is goal oriented … The structure that this book builds can allow a neophyte to avoid much of the initial confusion and wasted effort necessary to classify unfamiliar work and distinguish between what may be useful or not to one’s intents and interests. … an exquisitely enriched literature review that is almost good enough to use as an auxiliary graduate textbook … a rich yet accessible introduction …"
Computing Reviews, October 2014

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More About the Author

Haiping Lu received the B.Eng. and M.Eng degrees in Electrical and Electronic Engineering from Nanyang Technological University, Singapore, in 2001 and 2004, respectively, and the Ph.D. degree in Electrical and Computer Engineering from University of Toronto, Toronto, ON, Canada, in 2008.

Currently, he is an Assistant Professor with the Department of Computer Science at the Hong Kong Baptist University. His current research interests include machine learning, pattern recognition, big data, mobile computing, and biomedical engineering.

He is the leading author of the book "Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data" (CRC Press 2013). His work on EEG has been covered by the ACM TechNews, and he received the 2013 IEEE Computational Intelligence Society Outstanding PhD Dissertation Award.

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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
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