FAST KERNEL ICA | |--------------------| 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

fastKICA\amariD.m
........\demo.m
........\fastkica.m
........\Readme (2).txt
........\README.txt
........\source2.wav
........\source3.wav
........\source4.wav
........\utils\chol_gauss.c
........\.....\compDerivChol.m
........\.....\dChol.m
........\.....\dChol2.c
........\.....\dChol2Lin.c
........\.....\dCholLin.m
........\.....\dKmn.c
........\.....\dKmnLin.c
........\.....\getKern.c
........\.....\hessChol.m
........\.....\hsicChol.m
........\utils
fastKICA

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