A Fast Approximate Joint Diagonalization Algorithm Using a Criterion with a Block Diagonal Weight Matrix

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F08%3A00306560" target="_blank" >RIV/67985556:_____/08:00306560 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A Fast Approximate Joint Diagonalization Algorithm Using a Criterion with a Block Diagonal Weight Matrix

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A Fast Approximate Joint Diagonalization Algorithm Using a Criterion with a Block Diagonal Weight Matrix

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/1M0572" target="_blank" >1M0572: Data, algoritmy, rozhodování</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ICASSP 2008: IEEE International Conference on Acoustics, Speech, and Signal Processing

ISBN

978-1-4244-1483-3

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

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Název nakladatele

Conference Management Services

Místo vydání

Bryan

Místo konání akce

Las Vegas

Datum konání akce

30. 3. 2008

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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