  We deal with a model where a set of observations is obtained by a linear
superposition of unknown components called sources. The problem consists in
recovering the sources without knowing the linear transform. We extend the
well-known Independent Component Analysis (ICA) methodology. Instead of
assuming independent source components, we assume that the source vector is a
probability mixture of two distributions. Only one distribution satisfies the
ICA assumptions, while the other one is concentrated on a specific but unknown
support. Sample points from the latter are clustered based on a data-driven
distance in a fully unsupervised approach. A theoretical grounding is provided
through a link with the Christoffel function. Simulation results validate our
approach and illustrate that it is an extension of a formerly proposed method.
