Sparse bounded component analysis

Abstract

Bounded Component Analysis (BCA) is a recent approach which enables the separation of both dependent and independent signals from their mixtures. This article introduces a novel deterministic instantaneous BCA approach for the separation of sparse bounded sources. The separation problem is posed as a geometric maximization problem, where the objective is the volume ratio of two geometric objects related to the separator output samples, namely the principal hyperellipsoid and bounding l1 norm ball. The global maxima of the corresponding objective are proven to be perfect separators. The article also provides an iterative algorithm corresponding to this objective. The numerical experiments illustrate the potential benefit of the proposed approach relative to existing algorithms.