A fast approximate joint diagonalization algorithm using a criterion with a block diagonal weight matrix

We propose a new algorithm for Approximate Joint Diagonalization (AJD) with two main advantages over existing state-of-the-art algorithms: Improved overall running speed, especially in large-scale (high-dimensional) problems; and an ability to incorporate specially structured weight-matrices into the AJD criterion. The algorithm is based on approximate Gauss iterations for successive reduction of a weighted Least Squares off-diagonality criterion. The proposed Matlab ¢ implementation allows AJD of ten £¥¤¦¤¨§©£¥¤¦¤ matrices in 3-4 seconds (for the unweighted case) on a common PC (Pentium

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Petr Tichavský, Arie Yeredor, Jan Nielsen
• Keywords: Approximate Gauss Iterations, Approximate Joint Diagonalization, ICASSP 2008, Signal Processing, Weighted Least Squares