搜索资源列表
an_ica_tool
- 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 n
icaML
- 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 use
Basic ICA code in MATLAB (as used in Bell and Sejn
- 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.
icaMF
- 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]
Basic ICA code in MATLAB (as used in Bell and Sejn
- 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.
icaMF
- 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]
icaML
- 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 use
Bell_Sejnowski_ICA
- 两路语音信号的Bell-Sejnowski ICA算法 -Basic Bell-Sejnowski ICA algorithm demonstrated on 2 speech signals.
infomax
- informax ica算法，用于脑电信号识别-informax ica algorithm for signal recognition to make point
icaFacesCode
- Based on Bartlett, Movellan, & Sejnowski (2002). Face Recognition by Independent Component analysis.
sep96
- Source Code in Matlab implementing Source Separation as used in Bell and Sejnowski 1996.An information maximisation approach to blind separation and blind deconvolution.
Basic-ICA-code-
- 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
sfa_tk101.tar
- 慢特征分析 sfa matlb源程序 参考文献： Wiskott, L. and Sejnowski, T.J. (2002), "Slow Feature Analysis: Unsupervised Learning of Invariances",(Slow feature analysis SFA MATLB source program)