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fastKICA
说明： 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)
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chap11
syms x
g=exp(x*sin(x))
t=taylor(g,12,2)
xd = 1:0.05:3 yd = subs(g,x,xd)
ezplot(t, [1,3]) hold on
plot(xd, yd, r-. )
title( Taylor approximation vs. actual function )
legend( Taylor , Function )(syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' ))
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