Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation
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We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the information geometric log-linear model equipped with a hierarchically structured sample space, which has 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 on images that our approach is superior to current state-of-the-art techniques and is able to separate signals with complex interactions.
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