Blind separation of binary sources with less sensors than sources

Blind separation of unknown sources from their mixtures is currently a timely research topic in statistical signal processing and unsupervised neural learning. Several source separation algorithms have been presented where it is assumed that there are at least as many sensors as sources. In this paper, a practical algorithm is proposed for separating binary sources from less sensors than sources. The algorithm uses constrained competitive learning in the adaptation phase and the actual separation is achieved by simply selecting the best matching unit. The algorithm appears to be reasonably robust against small additive noise