Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
High-order contrasts for independent component analysis
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Kernel independent component analysis
The Journal of Machine Learning Research
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
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This work deals with the problem of blind source separation solved by minimization of mutual information. After having chosen a model for the mixture, we focus on two methods. One is based on the minimization of an estimation of I, the mutual information. The other one uses a minimization of an estimation of C, the mutual information after transforming all the joint entropy terms. We show the differences between these two approaches by studying statistical properties of the two estimators.In this paper, we derive the bias of the estimators of the two criteria I and C. It is shown that under the hypothesis of independence, the estimator of I is asymptotically unbiased even if the bandwidth is kept fixed, whereas with a fixed bandwidth the estimator of C is not asymptotically unbiased.Further, the minimization is achieved by a relative gradient descent method and we show the differences between criteria I and C through the expression of their relative gradients.