Abstract
In this paper we review a multilinear generalization of the singular value decomposition and the best rank-( R 1, R 2,…, R N ) approximation of higher-order tensors. We show that they are important tools for dimensionality reduction in higher-order signal processing. We discuss applications in independent component analysis, simultaneous matrix diagonalization and subspace variants of algorithms based on higher-order statistics.
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