Joint eigenvalue decomposition using polar matrix factorization

Authors:
Xavier Luciani;Laurent Albera
Affiliations:
Inserm, Rennes, France and Université de Rennes 1, LTSI, Rennes, France;Inserm, Rennes, France and Université de Rennes 1, LTSI, Rennes, France
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

In this paper we propose a new algorithm for the joint eigenvalue decomposition of a set of real non-defective matrices. Our approach resorts to a Jacobi-like procedure based on polar matrix decomposition. We introduce a new criterion in this context for the optimization of the hyperbolic matrices, giving birth to an original algorithm called JDTM. This algorithm is described in detail and a comparison study with reference algorithms is performed. Comparison results show that our approach provides quicker and more accurate results in all the considered situations.