@inproceedings{5f3c87d2a9884e7b9f02af53577c61b3,

title = "Joint block diagonalization algorithms for optimal separation of multidimensional components",

abstract = "This paper deals with non-orthogonal joint block diagonalization. Two algorithms which minimize the Kullback-Leibler divergence between a set of real positive-definite matrices and a block-diagonal transformation thereof are suggested. One algorithm is based on the relative gradient, and the other is based on a quasi-Newton method. These algorithms allow for the optimal, in the mean square error sense, blind separation of multidimensional Gaussian components. Simulations demonstrate the convergence properties of the suggested algorithms, as well as the dependence of the criterion on some of the model parameters.",

keywords = "Joint block diagonalization, quasi-Newton, relative gradient",

author = "Dana Lahat and Cardoso, {Jean Fran{\c c}ois} and Hagit Messer",

year = "2012",

doi = "10.1007/978-3-642-28551-6_20",

language = "אנגלית",

isbn = "9783642285509",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "155--162",

booktitle = "Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings",

note = "null ; Conference date: 12-03-2012 Through 15-03-2012",

}