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

Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos




[PDF.cg62] Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

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 | #2313633 in Books |  Chapman and Hall/CRC |  2013-12-11 | Original language:English | PDF # 1 |  9.30 x.90 x6.20l,.0 | File type: PDF | 296 pages

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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 builds an overarching framework and provides an 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 nat...

You can specify the type of files you want, for your device.Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)   |  Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos. Just read it with an open mind because none of us really know.

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