This paper addresses the problem of efficient information theoretic, non-parametric data clustering. We develop a procedure for adapting the cluster memberships of the data patterns, in order to maximize the recent Cauchy-Schwarz (CS) probability density function (pdf) distance measure. Each pdf corresponds to a cluster. The CS distance is estimated analytically and non-parametrically by means ...

Simulation and theoretical results show that memoryless threshold neurons benefit from small amounts of almost all types of additive noise and so produce the stochastic-resonance or SR effect. Input-output mutual information measures the performance of such threshold systems that use subthreshold signals. The SR result holds for all possible noise probability density functions with finite varia...

We present two new hyperbolic source probability models to effectively represent sub-gaussian and super-gaussian families of sources for dynamic and convolutive Blind Source Recovery (BSR). Both models share a common boundary for the gaussian density function. The proposed hyperbolic probability model for the sub-gaussian densities is an extension of the Pearson density model. The model can rep...