In this paper we discuss a multilinear generalization of the best rank-R approximation problem for matrices, namely, the approximation of a given higher-order tensor, in an optimal least-squares sense, by a tensor that has prespecified column rank value.Expand

This overview article aims to provide a good starting point for researchers and practitioners interested in learning about and working with tensors.Expand

In this paper we introduce a new class of tensor decompositions. Intuitively, we decompose a given tensor block into blocks of smaller size, where the size is characterized by a set of mode-$n$… Expand

In this paper we study two fourth-order cumulant-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis.Expand

We derive a new and relatively weak deterministic sufficient condition for uniqueness of higher-order tensors which have the property that the rank is smaller than the greatest dimension.Expand

In this paper we discuss a multilinear generalization of the best rank-R approximation problem for matrices, namely, the approximation of a given higher-order tensor, in an optimal leastsquares… Expand

We propose the emerging technique of independent component analysis, also known as blind source separation, as an interesting tool for the extraction of the antepartum fetal electrocardiogram from multilead cutaneous potential recordings.Expand

We study simultaneous matrix diagonalization-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis (ICA).Expand