Simultaneous diagonalization: the asymmetric, low-rank, and noisy settings
Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang · arXiv (Cornell University) · 2015
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.