Multi-Input Single-Output Nonlinear Blind Separation of Binary Sources

The problem of blindly separating multiple binary sources from a single nonlinear mixture is addressed through a novel clustering approach without the use of any optimization procedure. The method is based on the assumption that the source probabilities are asymmetric in which case the output probability distribution can be expressed as a linear mixture of the sources. We are then able to solve the problem by using a known linear Multiple-Input Single-Output (MISO) blind separation method. The overall procedure is very fast and, in theory, it works for any number of independent binary sources and for a wide range of nonlinear functions. In practice, the accuracy of the method depends on the estimation accuracy of the output probabilities and the cluster centers. It can be quite sensitive to noise especially as the number of sources increases or the number of data samples is reduced. However, in our experiments we have been able to demonstrate successful separation of up to four sources.