Non-orthogonal Simultaneous Diagonalization of K-Order Complex Tensors for Source Separation
Victor Maurandi, Éric Moreau · IEEE Signal Processing Letters · 2017
Source separation in the statistical framework is usually managed using tensor decompositions or matrix joint diagonalization. In this letter, we propose one of the first coordinate algorithms for non-orthogonal simultaneous diagonalization of any order complex tensors. It relies on the optimization of an inverse criterion and on a particular decomposition allowing to derive each parameter in an independent way. In the framework of digital telecommunication source separation, computer simulations show the interest of using sets of high-order tensors. They also illustrate the overall interesting performances of the proposed algorithm in comparison to a Jacobi-like algorithm of matrix joint diagonalization and to a canonical polyadic decomposition algorithm.