Bell and Sejnowski 在1996提出的Ica算法，用matlab實現的，但版本較舊，需要做修改才能用于新版本。
上傳時間： 2015-03-23
上傳用戶：tedo811
這是一個基于峰度的Ica算法，里面的Ica_kurt.m是主文件，輸入為待處理的觀察信號，要求是矩陣形式，行數為所包含的分量數，列數為每一個分量包含的抽樣點數。
上傳時間： 2013-12-27
上傳用戶：qilin
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.
標簽： instantaneous algorithm Bayesian Gaussian
上傳時間： 2013-12-19
上傳用戶：jjj0202
cardoso 的Ica編碼，可以用在人臉識別上面
上傳時間： 2014-12-03
上傳用戶：ggwz258
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
標簽： mathematIcal algorithms mechanIcal procedures
上傳時間： 2015-03-31
上傳用戶：silenthink
這次上傳的代碼是關于特征提取的主要算法之一：Ica，其比pca要好
上傳時間： 2013-12-10
上傳用戶：BIBI
Fast Ica，一種Ica的快速算法，有完整的論文（兩種格式，pdf和ps的）
上傳時間： 2015-04-06
上傳用戶：sy_jiadeyi
Ica can be used in brain activation studies to reduce the number of dimension and filter out independent and interesting activations. This demonstration shows two studies. One provided by Hvidovre Universitets Hospital, Denmark, that consists of fMRI scannings of humans. Another provided by the EU sponsored MAPAWAMO project from fMRI scannings of monkeys. In the demo comparison between IcaMS, IcaML, IcaMF, IcaMF (positive sources) and PCA can be made. More detailes can found in [2].
標簽： activation dimension studies indepen
上傳時間： 2015-04-19
上傳用戶：zukfu
Ica is used to classify text in extension to the latent semantic indexing framework. Ica show to align the context grouping structure well in a human sense [1], thus can be used for unsupervised classifIcation. The demonstration shows this on medIcal abstracts (MED dataset), that uses BIC to estimate the number of classes and produces keywords for each class. The IcaML algorithm is used.
標簽： Ica extension framework classify
上傳時間： 2013-12-22
上傳用戶：himbly
快速Ica算法，在盲源分離中有很大的用處。
上傳時間： 2015-05-22
上傳用戶：rocwangdp
