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Published in:
2010 | OriginalPaper | Chapter
Joint Eigenvalue Decomposition Using Polar Matrix Factorization
Authors : Xavier Luciani, Laurent Albera
Published in: Latent Variable Analysis and Signal Separation
Publisher: Springer Berlin Heidelberg
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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.