A novel deterministic method for large-scale blind source separation

A novel deterministic method for blind source separation is presented. In contrast to common methods such as independent component analysis, only mild assumptions are imposed on the sources. On the contrary, the method exploits a hypothesized (approximate) intrinsic low-rank structure of the mixing vectors. This is a very natural assumption for problems with many sensors. As such, the blind source separation problem can be reformulated as the computation of a tensor decomposition by applying a low-rank approximation to the tensorized mixing vectors. This allows the introduction of blind source separation in certain big data applications, where other methods fall short.