文件名称:kernel-ICA
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 82kb
- 下载次数：
- 0次
- 提 供 者：
- Jinxia******
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
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. -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.
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. -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.
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下载文件列表
kernel-ICA\amari_distance.m
..........\chol_gauss.c
..........\chol_gauss.m
..........\chol_hermite.c
..........\chol_hermite.m
..........\chol_poly.c
..........\chol_poly.m
..........\contrast_emp_grad.m
..........\contrast_emp_grad_oneunit.m
..........\contrast_ica.m
..........\contrast_ica_oneunit.m
..........\contrast_update_oneunit.m
..........\demo_kernel_ica.m
..........\.istributions\mybetarnd.m
..........\.............\myexprnd.m
..........\.............\mygamrnd.m
..........\.............\mymvnrnd.m
..........\.............\mynormrnd.m
..........\.............\myrndcheck.m
..........\.............\mytrnd.m
..........\.............\usr_distrib.m
..........\empder_search.m
..........\empder_search_oneunit.m
..........\FastBranchFlow.mat
..........\Gauss_Noise.mat
..........\global_mini.m
..........\global_mini_oneunit.m
..........\global_mini_sequential.m
..........\kernel_ica.m
..........\kernel_ica_options.m
..........\rand_orth.m
..........\readme.txt
..........\update_contrast.m
..........\WK.mat
..........\WKN.mat
..........\distributions
kernel-ICA
..........\chol_gauss.c
..........\chol_gauss.m
..........\chol_hermite.c
..........\chol_hermite.m
..........\chol_poly.c
..........\chol_poly.m
..........\contrast_emp_grad.m
..........\contrast_emp_grad_oneunit.m
..........\contrast_ica.m
..........\contrast_ica_oneunit.m
..........\contrast_update_oneunit.m
..........\demo_kernel_ica.m
..........\.istributions\mybetarnd.m
..........\.............\myexprnd.m
..........\.............\mygamrnd.m
..........\.............\mymvnrnd.m
..........\.............\mynormrnd.m
..........\.............\myrndcheck.m
..........\.............\mytrnd.m
..........\.............\usr_distrib.m
..........\empder_search.m
..........\empder_search_oneunit.m
..........\FastBranchFlow.mat
..........\Gauss_Noise.mat
..........\global_mini.m
..........\global_mini_oneunit.m
..........\global_mini_sequential.m
..........\kernel_ica.m
..........\kernel_ica_options.m
..........\rand_orth.m
..........\readme.txt
..........\update_contrast.m
..........\WK.mat
..........\WKN.mat
..........\distributions
kernel-ICA