  We present a novel blind source separation (BSS) method, called information
geometric blind source separation (IGBSS). Our formulation is based on the
log-linear model equipped with a hierarchically structured sample space, which
has theoretical guarantees to uniquely recover a set of source signals by
minimizing the KL divergence from a set of mixed signals. Source signals,
received signals, and mixing matrices are realized as different layers in our
hierarchical sample space. Our empirical results have demonstrated on images
and time series data that our approach is superior to well established
techniques and is able to separate signals with complex interactions.
