Blind separation of multiple binary sources from one nonlinear mixture

We propose a new method for the blind separation of multiple binary signals from a single general nonlinear mixture. In addition to the usual independence assumption on the input signals our key hypothesis is the asymmetry of the source probabilities. This condition allows us to express the output probability distribution as a linear mixture of the sources. We then proceed to solve the problem using known linear BSS methods for the binary underdetermined case. The method is based on clustering avoiding costly iterative optimization. Our simulations demonstrate successful separation for up to four sources. The problem however grows exponentially with the number of sources n, and the dataset size required for accurate estimation may become prohibitively large for large n.