fastKICA

说明：  盲源分离FastICA、matalab程序，(FAST KERNEL ICA | |--------------------| Version 1.0- February 2007 Copyright 2007 Stefanie Jegelka, Hao Shen, Arthur Gretton This package contains a Matlab implementation of the Fast Kernel ICA algorithm as described in [1]. Kernel ICA is based on minimizing a kernel measure of statistical independence, namely the Hilbert-Schmidt norm of the covariance operator in feature space (see [3]: this is called HSIC). Given an (n x m) matrix W of n samples from m mixed sources, the goal is to find a demixing matrix X such that the dependence between the estimated unmixed sources X *W is minimal. FastKICA uses an approximate Newton method to perfom this optimization. For more information on the algorithm, read [1], and for more information on HSIC, refer to [3]. The functions chol_gauss and amariD are taken from and based on, respectively, code from Francis Bach (available at http://cmm.ensmp.fr/~bach/kernel-ica/index.htm). The derivative is com)