Optimization of JADE using a novel optimally weighted joint diagonalization approach

Alexander Smekhov, Arie Yeredor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The JADE algorithm (Cardoso and Souloumiac, 1993) is a popular batch-type algorithm for Blind Source Separation (BSS), which employs approximate joint diagonalization (AJD) of fourth-order cumulant matrices, following a whitening stage. In this paper we propose a computationally attractive optimization of JADE for noiseless mixtures, in the form of a post-processing tool. First, we cast the AJD of 4th- and 2nd- order estimated matrices as a weighted least-squares (WLS) problem. We then show (under some commonly met conditions), that in the vicinity of a non-mixing condition (such as at the output of traditional JADE), the asymptotically optimal WLS criterion can be easily formulated and conveniently optimized via a novel algorithm, which uses non-unitary AJD of transformed subsets of the estimated matrices. Optimality with respect to general mixing is maintained, as we show, thanks to the equivariance of the optimal WLS solution. The performance of the new algorithm is analyzed and compared to JADE, identifying the conditions for most pronounced improvement, as demonstrated by simulation.

Original languageEnglish
Title of host publication2004 12th European Signal Processing Conference, EUSIPCO 2004
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages4
ISBN (Electronic)9783200001657
StatePublished - 3 Apr 2015
Event12th European Signal Processing Conference, EUSIPCO 2004 - Vienna, Austria
Duration: 6 Sep 200410 Sep 2004

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference12th European Signal Processing Conference, EUSIPCO 2004


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