Unsupervised Learning Algorithms and Latent Variable Models: PCA/SVD, CCA/PLS, ICA, NMF, etc.
Andrzej Cichocki, Laboratory for Advanced Brain Signal Processing, RIKEN, Brain Science Institute, Wako-shi, Saitama 3510198, Japan, cia@brain.riken.jp
Abstract
Constrained matrix and tensor factorizations, also called penalized matrix/tensor decompositions play a key role in Latent Variable Models (LVM), Multilinear Blind Source Separation (MBSS), and (multiway) Generalized Component Analysis (GCA) and they are important unifying topics in signal processing and linear and multilinear algebra. This chapter introduces basic linear and multilinear models for matrix and tensor factorizations and decompositions. The “workhorse” of this chapter ...
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