TY - GEN
T1 - On computation of approximate joint block-diagonalization using ordinary AJD
AU - Tichavský, Petr
AU - Yeredor, Arie
AU - Koldovský, Zbyněk
N1 - Funding Information: This work was supported by Ministry of Education, Youth and Sports of the Czech Republic through the project 1M0572 and by Grant Agency of the Czech Republic through the project 102/09/1278.
PY - 2012
Y1 - 2012
N2 - Approximate joint block diagonalization (AJBD) of a set of matrices has applications in blind source separation, e.g., when the signal mixtures contain mutually independent subspaces of dimension higher than one. The main message of this paper is that certain ordinary approximate joint diagonalization (AJD) methods (which were originally derived for "degenerate" subspaces of dimension 1) can also be used successfully for AJBD, but not all are suitable equally well. In particular, we prove that when the set is exactly jointly block-diagonalizable, perfect block-diagonalization is attainable by the recently proposed AJD algorithm "U-WEDGE" (uniformly weighted exhaustive diagonalization with Gaussian iteration) - but this basic consistency property is not shared by some other popular AJD algorithms. In addition, we show using simulation, that in the more general noisy case, the subspace identification accuracy of U-WEDGE compares favorably to competitors.
AB - Approximate joint block diagonalization (AJBD) of a set of matrices has applications in blind source separation, e.g., when the signal mixtures contain mutually independent subspaces of dimension higher than one. The main message of this paper is that certain ordinary approximate joint diagonalization (AJD) methods (which were originally derived for "degenerate" subspaces of dimension 1) can also be used successfully for AJBD, but not all are suitable equally well. In particular, we prove that when the set is exactly jointly block-diagonalizable, perfect block-diagonalization is attainable by the recently proposed AJD algorithm "U-WEDGE" (uniformly weighted exhaustive diagonalization with Gaussian iteration) - but this basic consistency property is not shared by some other popular AJD algorithms. In addition, we show using simulation, that in the more general noisy case, the subspace identification accuracy of U-WEDGE compares favorably to competitors.
UR - http://www.scopus.com/inward/record.url?scp=84857281884&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28551-6_21
DO - 10.1007/978-3-642-28551-6_21
M3 - منشور من مؤتمر
SN - 9783642285509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 171
BT - Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings
T2 - 10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012
Y2 - 12 March 2012 through 15 March 2012
ER -