Search - ICA

Search - ICA - List

【matlab】 fast ica 快速盲源分离 DL : 1: 快速ica算法
Update : 2010-12-11 Size : 41249 Publisher : ghyysu123
【Report papers】基于ICA的分析 DL : 0: 介绍利用ICA来去除眼电伪迹
Update : 2011-03-31 Size : 768925 Publisher : yong917916@126.com
【SourceCode】 ica算法 DL : 0: 此算法包括去均值，白化还有fastica过程，可以将混合图形分离出来。
Update : 2011-07-15 Size : 1570 Publisher : yuyaoshui
【matlab】盲源分离（快速ICA）算法 DL : 0: 盲源分离（快速ICA）算法，matlab代码
Update : 2011-10-26 Size : 42840 Publisher : tamujin@163.com
【Hook api】 ICA 信号分离 DL : 0: ICA 信号分离
Update : 2011-10-28 Size : 44149 Publisher : chen.carton@gmail.com
【Other】 FastICA_2.2 DL : 0: fast fixed-point ICA算法的Matlab实现-fast fixed-point algorithms ICA Matlab
Update : 2024-05-09 Size : 43008 Publisher : 田静
【Mathimatics-Numerical algorithms】 eeglab4.4.tar DL : 0: 含有多种ICA算法的eeglab工具箱-containing multiple ICA algorithm eeglab Toolbox
Update : 2024-05-09 Size : 1992704 Publisher : 田静
【Speech/Voice recognition/combine】 cubica DL : 0: cubica算法performes ICA by diagonalization of third- and fourth-order cumulants simultaneously-cubica algorithm performes ICA by diagonalization of third- and fourth-order cumulants simultaneously
Update : 2024-05-09 Size : 253952 Publisher : 王舒翀
【Speech/Voice recognition/combine】 ica_v0.04 DL : 0: ica语音分离算法，cadoso经典奉献-ICA algorithm of speech signal separating. contributed by Cadaso
Update : 2024-05-09 Size : 20480 Publisher : 王舒翀
【matlab】 fica5 DL : 1: 这是一个关于盲源分离独立成分分析方法（ICA）的软件包，给大家分享一下！-This is a share software package about blind signal separation (BSS) using independent component analysis(ICA).
Update : 2024-05-09 Size : 32768 Publisher : 杨涛
【Bio-Recognize】 ICA2000_reprint.doc DL : 0: 具有带通选择性的ICA算法可以改善对于带通时间序列的分离以及对于周期性脑功能响应信号的提取. 因此本文提出的方案可将被估计信号, 如:周期性响应信号以及具有平滑空间分布的脑功能激活区, 的先验特性以特征选择的方式加入ICA算法用以提高对此类信号的估计-with selective ICA algorithm can be improved for the band pass time series, as well as for the separation of brain function cyclical response signal extraction. Therefore this paper, the program can be estimated signals, such as : the cyclical response signal as well as smoothing the spatial distribution of activation of brain function, a priori properties Feature selection to join the ICA algorithm used to enhance the signal of such estimates
Update : 2024-05-09 Size : 280576 Publisher : 周周
【matlab】 YOLi_ICASSP05 DL : 0: 本文提出一种用于独立成份分析(ICA)的特征选择滤波方案用于改善ICA算法对关键独立成份(SOI)的分离和提取,关键独立成份在其信号样本数据的空间分布上具有一定特征. 本文以平滑滤波为例,表明加入此类特征滤波的ICA算法可以改善对于视觉功能区等平滑图象信号的提取. 因此, 这种特征滤波技术在估计具有平滑特性的脑功能成像方面具有潜在的应用价值.-for Independent component analysis (ICA) feature selection filtering program for the improvement of ICA algorithm independent of the key ingredients (SOI), the separation and extraction, the key element in its independent signal sample data on the spatial distribution of certain characteristics. This paper smoothing filter as an example, table membership in such features filtering ICA algorithm can improve visual function areas smoothing image signal extraction. Therefore, this feature filtering technology is estimated at smoothing properties of brain imaging have potential value.
Update : 2024-05-09 Size : 80896 Publisher : 周周
【Bio-Recognize】 YLiGRC04 DL : 0: 本文提出一种基于增广拉格朗日法的非线性约束优化算法用于独立成份分析(ICA), 仿真试验结果表明此方法可以有效的用于独立成份的分离. -This paper presents a broad-based increase Lagrange method of nonlinear constrained optimization algorithm for the independent component analysis (ICA), the simulation results show that this method can be effective for the separation of independent components.
Update : 2024-05-09 Size : 89088 Publisher : 周周
【AI-NN-PR】 runica DL : 0: 提出了一种利用S函数实验结果表明：ICA可以将脑电信号中包含的心电（ECG）、眼电（EOG）等多种干扰信号成功地分离出来-use of a S-function experimental results show : ICA EEG can be included in the heart (ECG), eyes (EOG) and other interference signal successfully separated
Update : 2024-05-09 Size : 12288 Publisher : 周周
【AI-NN-PR】 qiaoduu DL : 0: 基于ICA的信号分离程序，是一个很好的信号处理方面的程序-based ICA signal separation procedure is a very good signal processing procedures
Update : 2024-05-09 Size : 1024 Publisher : 陈茜
【AI-NN-PR】 ica453 DL : 0: Demonstration and test of the kernel-ica package
Update : 2024-05-09 Size : 14336 Publisher : 辜小花
【Wavelet】 icaMF DL : 0: 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.
Update : 2024-05-09 Size : 563200 Publisher : 陈互
【Bio-Recognize】 icaML DL : 0: This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
Update : 2024-05-09 Size : 7168 Publisher : 陈互
【matlab】 cardoso code DL : 0: cardoso 的ICA编码，可以用在人脸识别上面-Cardoso ICA coding, can be used in the above Face Recognition
Update : 2024-05-09 Size : 5120 Publisher : 毛克
【Bio-Recognize】 icalab DL : 0: The ICA/BSS algorithms are pure mathematical formulas, powerful, but rather mechanical procedures: There is not very much left for the user to do after the machinery has been optimally implemented. The successful and efficient use of the ICALAB strongly depends on a priori knowledge, common sense and appropriate use of the preprocessing and postprocessing tools. In other words, it is preprocessing of data and postprocessing of models where expertise is truly ne-Instruction : The ICA/BSS algorithms are pure mathematical formulas, powerful, but rather mechanical procedures: There is not very much left for the user to do after the machinery has been optimally implemented. The successful and efficient use of the ICALAB strongly depends on a priori knowledge, common sense and appropriate use of the preprocessing and postprocessing tools. In other words, it is preprocessing of data and postprocessing of models where expertise is truly needed.
Update : 2024-05-09 Size : 12275712 Publisher : 孙占全

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