A Jacobi-Like Algorithm for the General Joint Diagonalization Problem with Its Application to Blind Source Separation
Wenrui Li, Jifei Miao, Guanghui Cheng · 2019
The general problem of the approximate joint diagonalization of non-Hermitian matrices is considered. This problem mainly arises in the data model of the joint blind separation for two datasets. Based on a special parameterization of the two diagonalizing matrices and adapted approximations of the classical cost function, we establish a Jacobi-like algorithm. It may serve for the canonical polyadic decomposition (CPD) of a third-order tensor, and in some scenarios they can outperform traditional CPD methods. Simulation results show the competitive performance of the proposed algorithm.