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Mining the Bilinear Structure of Data with Approximate Joint Diagonalization

Louis Korczowski 1, * Florent Bouchard 1 Christian Jutten 1 Marco Congedo 1
* Corresponding author
1 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing
GIPSA-DIS - Département Images et Signal
Abstract : Approximate Joint Diagonalization of a matrix set can solve the linear Blind Source Separation problem. If the data possesses a bilinear structure, for example a spatio-temporal structure, transformations such as tensor decomposition can be applied. In this paper we show how the linear and bilinear joint diagonalization can be applied for extracting sources according to a composite model where some of the sources have a linear structure and other a bilinear structure. This is the case of Event Related Potentials (ERPs). The proposed model achieves higher performance in term of shape and robustness for the estimation of ERP sources in a Brain Computer Interface experiment.
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Last modification on : Wednesday, November 3, 2021 - 5:09:06 AM
Long-term archiving on: : Wednesday, November 30, 2016 - 2:29:18 PM

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  • HAL Id : hal-01357245, version 1

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Louis Korczowski, Florent Bouchard, Christian Jutten, Marco Congedo. Mining the Bilinear Structure of Data with Approximate Joint Diagonalization. EUSIPCO 2016 - 24th European Signal Processing Conference, EURASIP, Aug 2016, Budapest, Hungary. pp.667-671. ⟨hal-01357245⟩

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