资 源 简 介
资源描述 |--------------------|
| FAST KERNEL ICA |
|--------------------|
Version 1.0 - February 2007
MPL license, see below
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],
