Hierarchical Probabilistic Model for Blind Source Separation via Legendre Transformation

Simon Luo, Lamiae Azizi, Mahito Sugiyama

Keywords: blind source separation, log-linear model, energy-based model, information geometry

Abstract: We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the 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 and time series data that our approach is superior to well established techniques and is able to separate signals with complex interactions.

One-sentence Summary: A novel formulation of blind source separation using a hierarchical energy-based model.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1909.11294/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=3NJx72L2Yz