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Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop
Blind source separation and blind output decorrelation are two well-known problems in signal processing. For instantaneous mixtures, blind source separation is equivalent to a generalized eigen-decomposition, while blind output decorrelation can be considered as an iterative method of output orthogonalization. We propose a steepest descent procedure on a new cost function based on the Frobenius norm which measures the diagonalization of correlation matrices to perform blind source separation asdoi:10.1109/nnsp.1997.622431 fatcat:hrjcyjnohbgshhku4til2ltfja