Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings

2015/01/26 by Kuleshov, Volodymyr, Chaganty, Arun Tesjavi, Liang, Percy
#FOS: Mathematics #Numerical Analysis (math.NA)

paper · doi:10.48550/arxiv.1501.06318

Abstract

Simultaneous matrix diagonalization is used as a subroutine in many machine learning problems, including blind source separation and paramater estimation in latent variable models. Here, we extend algorithms for performing joint diagonalization to low-rank and asymmetric matrices, and we also provide extensions to the perturbation analysis of these methods. Our results allow joint diagonalization to be applied in several new settings.

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