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Block Jacobi-type methods for non-orthogonal joint diagonalisation
In this paper, we study the problem of non-orthogonal joint diagonalisation of a set of real symmetric matrices via simultaneous conjugation. A family of block Jacobi-type methods are proposed to optimise two popular cost functions for the non-orthogonal joint diagonalisation, namely, the off-norm function and the log-likelihood function. By exploiting the appropriate underlying manifold, namely the so-called oblique manifold, rigorous analysis shows that, under the exact non-orthogonal joint diagonalisation setting, the proposed methods converge locally quadratically fast to a joint diagonaliser. Finally, performance of our methods is investigated by numerical experiments for both exact and approximate non-orthogonal joint diagonalisation.
@inproceedings{5632, title = {Block Jacobi-type methods for non-orthogonal joint diagonalisation}, journal = {Proceedings of the 34th International Conference on Acoustics, Speech, and Signal Processing (ICASSP09)}, booktitle = {ICASSP09}, abstract = {In this paper, we study the problem of non-orthogonal joint diagonalisation of a set of real symmetric matrices via simultaneous conjugation. A family of block Jacobi-type methods are proposed to optimise two popular cost functions for the non-orthogonal joint diagonalisation, namely, the off-norm function and the log-likelihood function. By exploiting the appropriate underlying manifold, namely the so-called oblique manifold, rigorous analysis shows that, under the exact non-orthogonal joint diagonalisation setting, the proposed methods converge locally quadratically fast to a joint diagonaliser. Finally, performance of our methods is investigated by numerical experiments for both exact and approximate non-orthogonal joint diagonalisation.}, pages = {3285-3288}, publisher = {IEEE Service Center}, organization = {Max-Planck-Gesellschaft}, institution = {Institute of Electrical and Electronics Engineers}, school = {Biologische Kybernetik}, address = {Piscataway, NJ, USA}, month = apr, year = {2009}, slug = {5632}, author = {Shen, H. and H{\"u}per, K.}, month_numeric = {4} }