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Convex Optimization for Blind Source Separation on a Statistical Manifold
Simon Luo · lamiae azizi · Mahito Sugiyama

Fri Dec 11 12:00 PM -- 01:00 PM (PST) @ None

We present a novel blind source separation (BSS) method using a hierarchical structure of sample space that is incorporated with a log-linear model. Our approach is formulated as a convex optimization with theoretical guarantees to uniquely recover a set of source signals by minimizing the KL divergence from a set of mixed signals. Source signals, received signals, and mixing matrices are realized as different layers in our hierarchical sample space. Our empirical results have demonstrated superiority compared to well established techniques.

Author Information

Simon Luo (The University of Sydney)
lamiae azizi (School of Mathematics and Statistics, The university of sydney)
Mahito Sugiyama (National Institute of Informatics)

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