Flexible independent component analysis
Seungjin Choi, Andrzej S Cichocki, Шун-ичи Амари · 2002
We present a flexible independent component analysis (ICA) algorithm which can separate mixtures of sub- and super-Gaussian source signals with self-adaptive nonlinearities. A flexible ICA algorithm, in the framework of natural Riemannian gradient, is derived using the parametrized generalized Gaussian density model. The nonlinear function in the flexible ICA algorithm is self-adaptive and is controlled by Gaussian exponent. Computer simulation results confirm the validity and high performance of the proposed algorithm.