Mixing and non-mixing local minima of the entropy contrast for blind source separation

In this paper, both non-mixing and mixing local minima of the entropy are analyzed from the viewpoint of blind source separation (BSS); they correspond respectively to acceptable and spurious solutions of the BSS problem. The contribution of this work is twofold. First, a Taylor development is used to show that the \textit{exact} output entropy cost function has a non-mixing minimum when this output is proportional to \textit{any} of the non-Gaussian sources, and not only when the output is proportional to the lowest entropic source. Second, in order to prove that mixing entropy minima exist when the source densities are strongly multimodal, an entropy approximator is proposed. The latter has the major advantage that an error bound can be provided. Even if this approximator (and the associated bound) is used here in the BSS context, it can be applied for estimating the entropy of any random variable with multimodal density.Comment: 11 pages, 6 figures, To appear in IEEE Transactions on Information Theor

Can we always trust entropy minima in the ICA context ?

Marginal entropy can be used as cost function for blind source separation (BSS). Recently, some authors have experimentally shown that such information-theoretic cost function may have spurious minima in specific situations. Hence, one could face spurious solutions of the BSS problem even if the mixture model is known, exactly as when using the maximum-likelihood criterion. Intuitive justifications of the spurious minima have been proposed, when the sources have multimodal densities. This paper aims to give mathematical arguments, complementary to existing simulation results, to explain the existence of such minima. This is done by first deriving a specific entropy estimator. Then, this estimator, although reliable only for multimodal sources with small-overlapping Gaussian modes, allows one to show that spurious minima may exist when dealing with such sources