盲源分离FastICA、matalab程序，-FAST KERNEL ICA　　|

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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