Description: 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
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