Tensor Decompositions Tools for Multidimensional CS

CESAR F. CAIAFA; ANDRZEJ CICHOCKI

Lausanne

Conferencia; Signal Processing with Adaptive Sparse Structured Representations - SPARS 2013; 2013

EPFL

Tensor decomposition models for multidimensional datasets have a long history in Mathematics and applied sciences. While these models have recently been applied to multidimensional signal processing, they were developed independently of the theory of sparse representations and Compressed Sensing (CS). We discuss, and illustrate recent results revealing connections among tensor decompositions models, recovery of low-rank multidimensional signals and CS theory.