Efficient source adaptivity in independent component analysis
Nikos Vlassis, Yoichi Motomura · IEEE Transactions on Neural Networks · 2001
A basic element in most independent component analysis (ICA) algorithms is the choice of a model for the score functions of the unknown sources. While this is usually based on approximations, for large data sets it is possible to achieve "source adaptivity" by directly estimating from the data the "true" score functions of the sources. We describe an efficient scheme for achieving this by extending the fast density estimation method of Silverman (1982). We show with a real and a synthetic experiment that our method can provide more accurate solutions than state-of-the-art methods when optimization is carried out in the vicinity of the global minimum of the contrast function.