Auxiliary-function-based independent component analysis for super-Gaussian sources

Authors:
Nobutaka Ono;Shigeki Miyabe
Affiliations:
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

This paper presents new algorithms of independent component analysis (ICA) for super-Gaussian sources based on auxiliary function technique. The algorithms consist of two alternative updates: 1) update of demixing matrix and 2) update of weighted covariance matrix, which include no tuning parameters such as step size. The monotonic decrease of the objective function at each update is guaranteed. The experimental results show that the derived algorithms are robust to nonstationary data and outliers, and the convergence is faster than natural-gradient-based algorithm.