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  4. A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation
 
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2004
Journal Article
Title

A fast algorithm for joint diagonalization with non-orthogonal transformations and its application to blind source separation

Abstract
A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem, and addresses diagonalization with a general, non-orthogonal transformation. The iterative scheme of the algorithm is based on a multiplicative update which ensures the invertibility of the diagonalizer. The algorithm's efficiency stems from the special approximation of the cost function resulting in a sparse, block-diagonal Hessian to be used in the computation of the quasi-Newton update step. Extensive numerical simulations illustrate the performance of the algorithm and provide a comparison to other leading diagonalization methods. The results of such comparison demonstrate that the proposed algorithm is a viable alternative to existing state-of-the-art joint diagonalization algorithms. The practical use of our algorithm is shown for blind source separation problems.
Author(s)
Ziehe, A.
Laskov, P.
Nolte, G.
Müller, K.-R.
Journal
Journal of Machine Learning Research  
Language
English
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