bibtype	C - Conference Paper (international conference)
ARLID	0306560
utime	20240111140700.5
mtime	20080410235959.9
title (primary) (eng)	A Fast Approximate Joint Diagonalization Algorithm Using a Criterion with a Block Diagonal Weight Matrix
specification	page_count 4 s. media_type CD-ROM
serial	ARLID cav_un_epca*0306559 ISBN 978-1-4244-1483-3 ISBN 1-4244-1484-9 title ICASSP 2008: IEEE International Conference on Acoustics, Speech, and Signal Processing page_num 3321-3324 publisher place Bryan name Conference Management Services year 2008
title (cze)	Rychlý algoritmus pro přibližnou vzájemnou diagonalizaci, používající kriterium s blokově diagonální váhovou maticí
keyword	approximate joint diagonalization
keyword	blind source separation
keyword	autoregressive processes
author (primary)	ARLID cav_un_auth*0101212 name1 Tichavský name2 Petr institution UTIA-B full_dept Department of Stochastic Informatics fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0213973 name1 Yeredor name2 A. country IL
author	ARLID cav_un_auth*0213020 name1 Nielsen name2 Jan institution UTIA-B fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	source_type textový dokument source_size 251kB
cas_special	project project_id 1M0572 agency GA MŠk ARLID cav_un_auth*0001814 research CEZ:AV0Z10750506 abstract (eng) We propose a new algorithm for Approximate Joint Diagonalization (AJD) with two main advantages over existing state-of-the-art algorithms: Improved overall running speed, especially in large-scale (high-dimensional) problems; and an ability to incorporate specially structured weight-matrices into the AJD criterion. The algorithm is based on approximate Gauss iterations for successive reduction of a weighted Least Squares off-diagonality criterion. The proposed Matlab implementation allows AJD of ten 100x100 matrices in 3-4 seconds (for the unweighted case) on a common PC (Pentium M, 1.86GHz, 2GB RAM), generally 3-5 times faster than the fastest competitor. The ability to incorporate weights allows fast large-scale realization of optimized versions of classical blind source separation algorithms, such as Second-Order Blind Identification (SOBI), whose weighted version (WASOBI) yields significantly improved separation performance. abstract (cze) V práci je navržen nový algoritmus pro vzájemnou diagonalizaci matic, který má oproti existujícím algoritmům dvě hlavní výhody: zlepšená rychlost výpočtu, zvláště u matic s vysokou dimenzí, a možnost využití specielně strukturovaných váhových matic v diagonalizačním kriteriu. Navržená implementace algoritmu v protředí Matlab umožnuje diagonalizaci 10 matic o velikosti 100 x 100 na běžném PC (Pentium M, 1.86GHz, 2GB RAM) - v průměru je 2-5x rychlejší než dosud nejrychlejší alternativy. Algoritmus umožňuje rychlou implementaci algoritmu WASOBI pro slepou separaci nezávislých autoregresních procesů s různými spektry. action ARLID cav_un_auth*0238885 name ICASSP 2008, IEEE International Conference on Acoustics, Speech adn Signal Processing place Las Vegas dates 30.03.2008-04.04.2008 country US reportyear 2010 RIV BB permalink http://hdl.handle.net/11104/0004577 arlyear 2008 mrcbU56 textový dokument 251kB mrcbU63 cav_un_epca*0306559 ICASSP 2008: IEEE International Conference on Acoustics, Speech, and Signal Processing 978-1-4244-1483-3 3321 3324 Bryan Conference Management Services 2008