Description: 一个ICA工具。This binary version of the runica() function of Makeig et al. contained
in the EEG/ICA Toolbox runs 12x faster than the Matlab version. It uses
the logistic infomax ICA algorithm of Bell and Sejnowski, with natural
gradient and extended ICA extensions. It was programmed for unsupervised
usage by Scott Makeig at CNL, Salk Institute, La Jolla CA. Sigurd Enghoff
translated it into C++ code and compiled it for multiple platforms. J-R
Duann has improved the PCA dimension-reduction and has compiled the
linux and free_bsd versions.
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Size: 136032 |
Author:aaaaaaa |
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Description: he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
Platform: |
Size: 7730 |
Author:薛耀斌 |
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Description: Bell and Sejnowski 在1996提出的ica算法，用matlab实现的，但版本较旧，需要做修改才能用于新版本。-Bell and Sejnowski 1996 in the ica algorithm, using Matlab to achieve, but the older version needs to be done in order for the new revised version. Platform: |
Size: 3527 |
Author:wyh |
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Description: ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction. Platform: |
Size: 563873 |
Author:陈互 |
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Description: Bell and Sejnowski 在1996提出的ica算法，用matlab实现的，但版本较旧，需要做修改才能用于新版本。-Bell and Sejnowski 1996 in the ica algorithm, using Matlab to achieve, but the older version needs to be done in order for the new revised version. Platform: |
Size: 3072 |
Author:wyh |
Hits:
Description: ICA算法The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.-ICA algorithm:The algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction. Platform: |
Size: 563200 |
Author:陈互 |
Hits:
Description: he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization.
The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.
Platform: |
Size: 7168 |
Author:薛耀斌 |
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Description: Source Code in Matlab implementing Source Separation as used in Bell and Sejnowski 1996.An information maximisation approach to blind separation and blind deconvolution. Platform: |
Size: 3072 |
Author:duantaotao |
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Description: ICA程序，1996提出的ica算法\Basic ICA code in MATLAB (as used in Bell and Sejnowski 1996).-Basic ICA code Platform: |
Size: 3072 |
Author:刘少华 |
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Search - Bell and Sejnowski