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This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning MSL for dimensionality reduction of multidimensional data directly from their tensorial representations.
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It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.
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- Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data.
Chapter 3 Fundamentals of Multilinear Subspace Learning. Chapter 4 Overview of Multilinear Subspace Learning.
Chapter 5 Algorithmic and Computational Aspects. Chapter 6 Multilinear Principal Component Analysis.
Multilinear Subspace Learning
Chapter 7 Multilinear Discriminant Analysis. Chapter 9 Applications of Multilinear Subspace Learning. Appendix A Mathematical Background.
Appendix B Data and Preprocessing.
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