Multilinear Subspace Learning

Emphasizing essential concepts and system-level perspectives, this book provides a foundation for solving many of today's most interesting and challenging problems in big multidimensional data processing. It gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Supporting materials are available online.

Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos

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.