文件名称:vbICA_1.0
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 1013kb
- 下载次数：
- 0次
- 提 供 者：
- c**
- 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
基于变分贝叶斯的独立成分分析
模型包括一般的高斯混合模型以及高斯混合模型与隐马尔科夫结合在一起的-Based on variational Bayesian independent component analysis model consists of a general Gaussian mixture Gaussian mixture model and hidden Markov models and combined
模型包括一般的高斯混合模型以及高斯混合模型与隐马尔科夫结合在一起的-Based on variational Bayesian independent component analysis model consists of a general Gaussian mixture Gaussian mixture model and hidden Markov models and combined
(系统自动生成,下载前可以参看下载内容)
下载文件列表
vbICA_1.0\Demos\test.mat
.........\.....\vbica_demo.m
.........\.....\vbmoica_demo.m
.........\MixModel1d\FB.m
.........\..........\gammas.m
.........\..........\hmmdecode1.m
.........\..........\hmm_mstep.m
.........\..........\initialise_mix1d.m
.........\..........\init_MoG.m
.........\..........\init_MoG1.m
.........\..........\init_MoP.m
.........\..........\learn_mix1d.m
.........\..........\log_ptilde1.m
.........\..........\log_ptilde2.m
.........\..........\mixmodel1d.m
.........\README
.........\Shared\initialise_ica.m
.........\......\init_vbica.m
.........\......\learn_matrix.m
.........\......\learn_noise.m
.........\......\NFEpos.m
.........\......\rect_expect.m
.........\......\update_alpha.m
.........\......\update_mean.m
.........\utils\decorr.m
.........\.....\digamma.c
.........\.....\digamma.dll
.........\.....\digamma.m
.........\.....\digamma.mexglx
.........\.....\digamma.mexsol
.........\.....\kmeans.m
.........\.....\kmeans1.m
.........\.....\my_gmm.m
.........\.....\my_gmmem.m
.........\.....\normalise.m
.........\.....\plotMoE.m
.........\.....\plotMoG.m
.........\.....\plotRMoG.m
.........\.....\plotsignals.m
.........\.....\plotSrc.m
.........\.....\rdiv.m
.........\.....\rsum.m
.........\.....\sampgauss.m
.........\.....\scale01.m
.........\.....\scalexp.m
.........\.....\scatnxm.m
.........\Variational Bayesian Independent Component Analysis Package 1.pdf
.........\vbHMM\hmmdecode.m
.........\.....\hmminit.m
.........\.....\hmmtrain.m
.........\.....\ICA_like.m
.........\.....\ICA_update.m
.........\.....\vbhmm.m
.........\..ICA1\learn_ica1.m
.........\......\recon_source1.m
.........\......\unmix1.m
.........\......\vbica1.m
.........\.....2\avge_like.m
.........\......\image.mat
.........\......\learn_ica2.m
.........\......\recon_source2.m
.........\......\unmix2.m
.........\......\vbica2.m
.........\..MoICA\initialise_mica.m
.........\.......\learn_mica.m
.........\.......\unmix.m
.........\.......\update_eta.m
.........\.......\vbmoica.m
.........\Demos
.........\MixModel1d
.........\Shared
.........\utils
.........\vbHMM
.........\vbICA1
.........\vbICA2
.........\vbMoICA
vbICA_1.0
.........\.....\vbica_demo.m
.........\.....\vbmoica_demo.m
.........\MixModel1d\FB.m
.........\..........\gammas.m
.........\..........\hmmdecode1.m
.........\..........\hmm_mstep.m
.........\..........\initialise_mix1d.m
.........\..........\init_MoG.m
.........\..........\init_MoG1.m
.........\..........\init_MoP.m
.........\..........\learn_mix1d.m
.........\..........\log_ptilde1.m
.........\..........\log_ptilde2.m
.........\..........\mixmodel1d.m
.........\README
.........\Shared\initialise_ica.m
.........\......\init_vbica.m
.........\......\learn_matrix.m
.........\......\learn_noise.m
.........\......\NFEpos.m
.........\......\rect_expect.m
.........\......\update_alpha.m
.........\......\update_mean.m
.........\utils\decorr.m
.........\.....\digamma.c
.........\.....\digamma.dll
.........\.....\digamma.m
.........\.....\digamma.mexglx
.........\.....\digamma.mexsol
.........\.....\kmeans.m
.........\.....\kmeans1.m
.........\.....\my_gmm.m
.........\.....\my_gmmem.m
.........\.....\normalise.m
.........\.....\plotMoE.m
.........\.....\plotMoG.m
.........\.....\plotRMoG.m
.........\.....\plotsignals.m
.........\.....\plotSrc.m
.........\.....\rdiv.m
.........\.....\rsum.m
.........\.....\sampgauss.m
.........\.....\scale01.m
.........\.....\scalexp.m
.........\.....\scatnxm.m
.........\Variational Bayesian Independent Component Analysis Package 1.pdf
.........\vbHMM\hmmdecode.m
.........\.....\hmminit.m
.........\.....\hmmtrain.m
.........\.....\ICA_like.m
.........\.....\ICA_update.m
.........\.....\vbhmm.m
.........\..ICA1\learn_ica1.m
.........\......\recon_source1.m
.........\......\unmix1.m
.........\......\vbica1.m
.........\.....2\avge_like.m
.........\......\image.mat
.........\......\learn_ica2.m
.........\......\recon_source2.m
.........\......\unmix2.m
.........\......\vbica2.m
.........\..MoICA\initialise_mica.m
.........\.......\learn_mica.m
.........\.......\unmix.m
.........\.......\update_eta.m
.........\.......\vbmoica.m
.........\Demos
.........\MixModel1d
.........\Shared
.........\utils
.........\vbHMM
.........\vbICA1
.........\vbICA2
.........\vbMoICA
vbICA_1.0