A Jacobi Like Algorithm for the General Joint Diagonalization Problem with Its Application to Blind Source Separation
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.
2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP BMEI)