A new General Weighted Least-Squares Algorithm for Approximate Joint Diagonalization

Marco Congedo 1, * Christian Jutten 1 Reza Sameni 1 Cedric Gouy-Pailler 1
* Corresponding author
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
Abstract : Independent component analysis (ICA) and other blind source separation (BSS) methods are important processing tools for multi-channel processing of electroencephalographic data and have found numerous applications for brain-computer interfaces. A number of solutions to the BSS problem are achieved by approximate joint diagonalization (AJD) algorithms, thus the goodness of the solution depends on them. We present a new least-squares AJD algorithm with adaptive weighting on the separating vectors. We show that it has good properties while keeping the greatest generality among AJD algorithms; no constraint is imposed either on the input matrices or on the joint diagonalizer to be estimated. The new cost function allows interesting extensions that are now under consideration.
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Marco Congedo, Christian Jutten, Reza Sameni, Cedric Gouy-Pailler. A new General Weighted Least-Squares Algorithm for Approximate Joint Diagonalization. Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course 2008, Sep 2008, Graz, Austria. pp.98-103. ⟨hal-00324215⟩

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