基于希尔波特独立性准则的ICA算法

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],

fastKICA
fastKICA