Hierarchical probabilistic model for blind source separation via Legendre transformation

Simon Luo, Lamiae Azizi, Mahito Sugiyama
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:312-321, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-luo21a, title = {Hierarchical probabilistic model for blind source separation via Legendre transformation}, author = {Luo, Simon and Azizi, Lamiae and Sugiyama, Mahito}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {312--321}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/luo21a/luo21a.pdf}, url = {https://proceedings.mlr.press/v161/luo21a.html}, 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.} }
Endnote
%0 Conference Paper %T Hierarchical probabilistic model for blind source separation via Legendre transformation %A Simon Luo %A Lamiae Azizi %A Mahito Sugiyama %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-luo21a %I PMLR %P 312--321 %U https://proceedings.mlr.press/v161/luo21a.html %V 161 %X 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.
APA
Luo, S., Azizi, L. & Sugiyama, M.. (2021). Hierarchical probabilistic model for blind source separation via Legendre transformation. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:312-321 Available from https://proceedings.mlr.press/v161/luo21a.html.

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