kernel-ICA-Matlab工具箱.rar

+------------+ | Kernel ICA | +------------+ Version 1.0 - March 19th, 2002 ------------------------------ Description ----------- The kernel-ica package is a Matlab program that implements the Kernel ICA algorithm for independent component analysis (ICA). The Kernel ICA algorithm is based on the minimization of a contrast function based on kernel ideas. A contrast function measures the statistical dependence between components, thus when applied to estimated components and minimized over possible demixing matrices, components that are as independent as possible are found. For more information, please read the following paper: Francis R. Bach, Michael I. Jordan (2001). Kernel Independent Component Analysis, Technical Report UCB//CSD-01-1166, University of California, Berkeley. The kernel-ica package is Copyright (c) 2002 by Francis Bach. If you have any questions or comments regarding this package, or if you want to report any bugs, please send me an e-mail to fbach@cs.berkeley.edu. The current version 1.0 has been released on March, 19th 2002. It has been tested on both matlab 5 and matlab 6. Check regularly the following for newer versions: http://www.cs.berkeley.edu/~fbach Installation ------------ 1. Unzip all the .m files in the same directory 2. (Optional) if you want a faster implementation which uses pieces of C code: at the matlab prompt, in the directory where the package is installed, type: mex chol_gauss.c It should create a compiled file whose extension depends on the platform you are using: Windows: chol_gauss.dll Solaris: chol_gauss.mexsol Linux : chol_gauss.mexglx To check if the file was correcly compiled, type which chol_gauss and the name of the compiled version should appear. If you have any problems with the C file of if you are using a platform i did not mention, please e-mail me. How to use the kernel-ica package --------------------------------- The function that you should use to run the ICA alforithm is 'kernel_ica'. a detailed description of its options are described inside the file and can be reached by simply typing 'help kernel_ica' at the matlab prompt. A simple demonstration script is provided : 'demo_kernel_ica'. NB: all the data should be given in columns, that is, if you have m components and N samples, the matrix should be m x N. If you wish to investigate the tools and methods we used for this algorithms, you will find the following files useful: -contrast_ica.m : computation of the contrast functions based on Kernel canonical correlations -chol_gauss.c/.m : incomplete cholesky decomposition