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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
De Lathauwer, L., & Castaing, J. (2006). Second-Order Blind Identification of Underdetermined Mixtures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 40–47). Springer Verlag. https://doi.org/10.1007/11679363_6
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