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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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The Haar transform can be expressed in the matrix form as T = HIHT (inverse transform HTTH) where I is an NxN image matrix, H is an NxN transformation matrix and T is the resulting NxN transform.
For Haar transform, the transformation matrix H contains the Haar basis functions hk(x). They are defined on a continuous, closed interval, x -[0, 1] for k = 0,1,2 ...,N -1 where N = 2n.(
The Haar transform can be expressed in the matrix form as T = HIHT (inverse transform HTTH) where I is an NxN image matrix, H is an NxN transformation matrix and T is the resulting NxN transform.
For Haar transform, the transformation matrix H contains the Haar basis functions hk(x). They are defined on a continuous, closed interval, x-[0, 1] for k = 0,1,2 ...,N-1 where N = 2n.)
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