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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: infomax的ICA算法的扩展程序源码
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Size: 21815 |
Author: 林峰 |
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Description: 用于脑电信号特征提取的InfoMax Algorithm Based on ICA；也可以稍作改动用于其他信息提取。-for feature extraction InfoMax Algorithm Based on ICA; Minor modifications can be used to extract other information.
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Size: 217816 |
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.
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Size: 563873 |
Author: 陈互 |
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Description: 用于脑电信号特征提取的InfoMax Algorithm Based on ICA；也可以稍作改动用于其他信息提取。-for feature extraction InfoMax Algorithm Based on ICA; Minor modifications can be used to extract other information.
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Size: 217088 |
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.
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Size: 563200 |
Author: 陈互 |
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Description: infomax 算法，盲信号处理中很有帮助-INFOMAX algorithm, blind signal processing helpful
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Size: 3072 |
Author: 黄炳国 |
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Description: 核ICA的工具箱，用于独立分分量分析，盲源信号分离（BSS）-nuclear ICA toolbox for independent sub-component analysis, Blind Source Separation (BSS)
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Size: 20480 |
Author: 李国齐 |
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Description: 一种新的ica算法，主要是应用与盲分离算法，大家可以-ica a new algorithm, and are primarily used to blind separation algorithm, we can s
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Size: 362496 |
Author: sunz |
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Description: infomax的ICA算法的扩展程序源码-Infomax ICA algorithm for the expansion of the program source
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Size: 21504 |
Author: 林峰 |
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Description: 基于Informax判据的ICA算法，属于独立分量分析的一种。-Informax the ICA-based criterion algorithm, belonging to an independent component analysis.
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Size: 7168 |
Author: 深蓝色 |
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Description: informax ica算法，用于脑电信号识别-informax ica algorithm for signal recognition to make point
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Size: 2048 |
Author: 曹磊 |
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Description: 一个非常经典的独立元分析程序包，可以应用在图像处理，故障诊断-a very classic ICA program packet ,which can be used in picture processing and fault diagnosis
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Size: 5120 |
Author: yang |
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Description: compute the Independent component analysis (ICA)
with the algorithm of infomax
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Size: 2048 |
Author: sak |
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Description: 基于Maximum likelihood准则的ICA算法 -the ICA algorithm based on Maximum likelihood
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Size: 7168 |
Author: zhou |
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Description: 独立主成分分析与主成分分析代码，速度比较快，而且比较好用-In these first experiments, both ICA and whitened PCA are used to compress the data, and all the components are used for classifying the examples. The classifier used is a 1-NN with Euclidean distance. The results shown in next sections are clear: when a rotational invariant classifier is used (as 1-NN with L2-norm) the classification results of FastICA/whitened PCA are equivalent, while the difference between Infomax and whitened data es significant but small
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Size: 69632 |
Author: 李阳 |
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Description: 极大化非高斯性的infomax ICA。和好很强大，希望对大家有所帮助-The maximization of non-Gaussian nature infomax the ICA. Very powerful and good, we hope to help
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Size: 8192 |
Author: caibanggui |
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Description: infomax ICA的算法实现，并在其中给出了一个infomax ICA的算法用于图像边缘检测的例子-infomax ICA algorithm, which gives an infomax ICA algorithm for image edge detection example
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Size: 1165312 |
Author: 魏交统 |
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Description: 一种在盲源分离中用到的互信息最大化Ica算法的matlab程序-Mutual information maximization Ica used a blind source separation algorithm matlab program
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Size: 1024 |
Author: wangmeng |
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Description: 用于脑电信号特征提取的infomax算法，基于infomax的ica算法的代码(Infomax algorithm for feature extraction of EEG signals, code based on infomax's ica algorithm)
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Size: 16384 |
Author: 淡然smile |
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