A matrix joint diagonalization approach for complex independent vector analysis

Authors:
Hao Shen;Martin Kleinsteuber
Affiliations:
Department of Electrical Engineering and Information Technology, Technische Universität München, Germany;Department of Electrical Engineering and Information Technology, Technische Universität München, Germany
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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Abstract

Independent Vector Analysis (IVA) is a special form of Independent Component Analysis (ICA) in terms of group signals. Most IVA algorithms are developed via optimizing certain contrast functions. The main difficulty of these contrast function based approaches lies in estimating the unknown distribution of sources. On the other hand, tensorial approaches are efficient and richly available to the standard ICA problem, but unfortunately have not been explored considerably for IVA. In this paper, we propose a matrix joint diagonalization approach to solve the complex IVA problem. A conjugate gradient algorithm on an appropriate manifold setting is developed and investigated by several numerical experiments.