Locally Adaptive Algorithms for ICA and their Implementations
Andrzej S Cichocki, Шун-ичи Амари · 2002
The main purpose of this chapter is to describe and overview models and to present a family of practical and efficient associated adaptive or locally adaptive learning algorithms, which have special advantages of efficiency and/or simplicity and straightforward electronic implementations. Some of the described algorithms have special advantages in the case of noisy, badly scaled or ill-conditioned signals. The developed algorithms are extended for the case when the number of sources and their statistics are unknown. Finally, the problems of an optimal choice of nonlinear activation function and general local stability conditions are also discussed. In particular, we focus on simple locally adaptive Hebbian/anti-Hebbian learning algorithms and their implementations using multi-layer neural networks.