A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the
orthonormality constraint in a more natural way in the course of performing independent …

It is seemingly paradoxical to the classical definition of the independent component analysis
(ICA), that in reality, the true sources are often not strictly uncorrelated. With this in mind, this …

Matrix Joint Diagonalization (MJD) is a powerful approach for solving the Blind Source
Separation (BSS) problem. It relies on the construction of matrices which are diagonalized …

We offer a redesigned form of the multidimensional scaling (MDS) algorithm suitable to the
simultaneous tracking of a large number of targets with no a priori mobility models. First, we …

Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-
sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint …

Joint approximate diagonalization (JAD) of multiple matrices is a core problem in many
applications. In this work we propose two numerical methods for computing JAD, based on …

This paper studies the problem of Overdetermined Blind Source Separation (OdBSS), a
challenging problem in signal processing. It aims to recover desired sources from …

A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the
orthonormality constraint in a more natural way in the course of performing the independent …

In this work, we consider block-Jacobi methods with Newton steps in each subspace search
and prove their local quadratic convergence to a local minimum with non-degenerate …

This paper studies the problem of Non-symmetric Joint Diagonalization (NsJD) of matrices,
namely, jointly diagonalizing a set of complex matrices by one matrix multiplication from the …