Loss function for blind source separation-minimum entropy criterion and its generalized anti-Hebbian rules
In adaptive signal processing, the least-mean squares (LMS) algorithm has long been used in signal enhancement and noise cancellation but it cannot overcome the difficulty caused by the signal leakage into the reference input. Hence we have to explore more general statistical properties about the observed signals. This view corresponds to a statistical modeling of the signals using statistical measures such as a loss function, which is different from the mutual information. This paper proposes a new loss function based on generalized Gaussian distribution family, and derives new simple adaptive learning rules. Our separator based on the new generalized "anti-Hebbian rules" is also justified by the simulation on both artificial and real data with good performance.