Abstract
In this paper we show that the underdetermined ICA problem can be solved using a set of spatial covariance matrices, in case the sources have sufficiently different temporal autocovariance functions. The result is based on a link with the decomposition of higher-order tensors in rank-one terms. We discuss two algorithms and present theoretical bounds on the number of sources that can be allowed.
L. De Lathauwer holds a permanent research position with the French CNRS; he also holds a honorary position with the K.U.Leuven. This work is supported in part by the Research Council K.U.Leuven under Grant GOA-AMBioRICS, in part by the Flemish Government under F.W.O. Project G.0321.06, Tournesol 2005 – Project T20013, and F.W.O. research communities ICCoS, ANMMM, and in part by the Belgian Federal Science Policy Office under IUAP P5/22.
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De Lathauwer, L., Castaing, J. (2006). Second-Order Blind Identification of Underdetermined Mixtures. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_6
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