Geometric Ica Using Nonlinear Correlation and Mds

We describe a method of visualising geometrically the dependency structure of a distributed representation. The mutual information between each pair of components is estimated using a nonlinear correlation coefficient, in terms of which a distance measure is defined. Multidimensional scaling is then used to generate a spatial configuration that reproduces these distances, the end result being a spatial representation of the dependency between the components, from which an appropriate topology for the representation may be inferred. The method is applied to ICA representations of speech and music.