Separating two binary sources from a single nonlinear mixture

In this paper we present a novel blind method for separating two binary sources from a single, arbitrary nonlinear mixture. The method is analytical and does not involve nonlinear optimization. Our approach proceeds by linearizing the problem and extending known, clustering-based results from the linear binary BSS case to the nonlinear case. The proposed algorithm is computationally efficient. Due to the structure of the problem, the true sources are extracted together with a source product adding one more indeterminacy to the usual sign and order indeterminacy of the sources. In some applications (eg. imaging) this indeterminacy can be resolved by visual inspection.