Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data
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 techniqu
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data
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 techniqu
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data

Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data

Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data
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Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data

Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data

Overview

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 techniqu

Product Details

ISBN-13: 9781040197691
Publisher: CRC Press
Publication date: 12/11/2013
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Sold by: Barnes & Noble
Format: eBook
Pages: 296
File size: 5 MB

About the Author

Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos

Table of Contents

Introduction. Fundamentals and Foundations: Linear Subspace Learning for Dimensionality Reduction. Fundamentals of Multilinear Subspace Learning. Overview of Multilinear Subspace Learning. Algorithmic and Computational Aspects. Algorithms and Applications: Multilinear Principal Component Analysis. Multilinear Discriminant Analysis. Multilinear ICA, CCA, and PLS. Applications of Multilinear Subspace Learning. Appendices. Bibliography. Index.
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