Multilinear subspace learning : dimensionality reduction of multidimensional data / Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos.
"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 dimens...
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Full Text (via ProQuest) |
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Main Authors: | , , |
Format: | eBook |
Language: | English |
Published: |
Boca Raton, FL :
CRC Press/Taylor and Francis Group,
[2014]
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Series: | Chapman & Hall/CRC machine learning & pattern recognition series.
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Subjects: |
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
- Appendix A: Mathematical Background
- Appendix B: Data and Preprocessing
- Appendix C: Software.