A Simple and Robust Fastica Algorithm Using the Huber M-Estimator Cost Function
Jih-Cheng Chao, S.C. Douglas · 2006
In blind source separation and independent component analysis, it is desirable to select a separation criterion that results in a simple algorithm and achieves accurate and robust source estimates. In this paper, we propose to use the Huber M-estimator cost function as the contrast function within the FastICA algorithm of Hyvärinen and Oja. The algorithm obtained from this cost is particularly simple to implement. We establish key properties regarding the local stability of the algorithm for general non-Gaussian source distributions, and its separating capabilities are shown through analysis to be largely insensitive to the cost function’s threshold parameter. Simulations comparing the performance of this algorithm to standard FastICA implementations are given.