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：
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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. 相关搜索： MATLAB
kernal
ica
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. 相关搜索： MATLAB
kernal
ica
(系统自动生成,下载前可以参看下载内容)
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