A new fast Jacobi-like algorithm for non-orthogonal joint diagonalization of real-valued matrices based on a QR parameterization

Non-orthogonal joint diagonalization of a set of real-valued matrices holds a significant place in numerous blind processing issues as independent component analysis and source separation. In this paper, in order to solve this problem, we propose a novel Jacobi-like algorithm based on a QR parameterization. The primary objective of this iterative algorithm is to derive an analytical solution for each two-by-two diagonalizing sub-matrix using a suitable cost function. By computer simulations, we show that the presented algorithm performs well with respect to three other ones from literature including two Jacobi-like algorithms.