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Abstract : |
separation Previous approaches for the blind source separation problem have used independent component analysis making the separated components statistically independent. In this paper, a new contrast for blind source separation of natural signals is proposed, which measures the algorithmic complexity of the sources and also the complexity of the mixing mapping. No assumptions about underlying probability distributions of the sources are necessary. Instead, it is required that the independent source signals have low complexity, which is generally true for natural signals. Connection to previous approaches is shown by demonstrating that minimum mutual information coincides with minimizing complexity in a special case. An experiment is presented, where a diOEcult problem of separating correlated signals is considered. The complexity minimization method is seen to give clearly more accurate results than the reference method utilizing ICA. 1, |
