A LINEAR LEAST-SQUARES ALGORITHM FOR JOINT DIAGONALIZATION

We present a new approach to approximate joint diagonalization of a set of matrices. The main advantages of our method are computational efficiency and generality. We develop an iterative procedure, called LSDIAG, which is based on multiplicative updates and on linear least-squares optimization. The efficiency of our algorithm is achieved by the first-order approximation of the matrices being diagonalized. Numerical simulations demonstrate the usefulness of the method in general, and in particular, its capability to perform blind source separation without requiring the usual prewhitening of the data.

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