SIMULTANEOUS MIBASED ESTIMATION OF INDEPENDENT COMPONENTS AND OF THEIR DISTRIBUTIONS
Lu'is B. Almeida
luis.almeida@inesc.pt
In ICA and BSS methods, the mutual information (MI)
of the estimated components is one of the most desirable
measures of statistical dependence, for use as an objective
function. However, its use requires the estimation of the
statistical distributions of the components. Previous MI
based ICA methods have resorted to an apriori knowledge
of those distributions or to their approximation by trun
cated series expansions or by means of kernels. This pa
per presents a method for simultaneously estimating those
distributions and performing MIbased ICA, using a sin
gle network trained with a single objective function. The
method is able to correctly handle mixtures of subgaussian
and supergaussian sources.