搜索资源列表
newicar
- 我最近写的一篇论文的源码，主要是对带参考信号的 ICA算法的扩展，多参考信号的ICA固定点算法。-I recently wrote a paper source, mainly for reference signal with the ICA algorithm expansion more reference signal ICA fixed-point algorithms.
Classification-MatLab-Toolbox
- 模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值-pattern recognition Matlab toolbox, including SVM, ICA, PCA, NN pattern recognition algorithms, and so on, of great reference value
ICA_with_Reference
- We present the technique of the ICA with Reference (ICA-R) to extract an interesting subset of independent sources from their linear mixtures when some a priori information of the sources are available in the form of rough templates (references). T
NMFLABIP_ver1.2
- n this group, we have three different algorithms. They are able to process in sequential and simultaneous mode. More detail in choosing reference signals could be found in the Tips section. The extracted signals have available forms of references. It
DSP_SuanFaCanKao
- DSP算法参考，包括FFT，ICA等算法，非常实用的参考。-DSP algorithm for reference, including the FFT, ICA and other algorithms, a very useful reference.
icabss
- 一种高效快速的线性混合新河盲分离ICA算法，提供了6个源信号以供参考，分离效果很好-An efficient linear mixed fast ICA algorithm for blind source separation New River, providing 6 source signal for reference, a good separation effect
IndependentComponentAnalysisAlgorithmsandApplicati
- ICA独立成分分析是近年研究的主流，此资料室介绍ICA的基础资料-ICA Independent component analysis of the mainstream in recent years, the basis of this information Reference Room descr iption ICA
IndependentComponentAnalysisanewconcept
- ica 的基础性文献， 对理解ica原理 及发展很有参考价值。-ica of the basic literature, theory and development in understanding ica useful reference.
ICA
- ICA盲源分离，用于信号的处理，可以实现混叠信号的分离，外文文献，具有较好的参考-ICA blind source separation, for signal processing, can achieve the separation of overlapping signals, foreign literature, with a better reference
ICAICA-fixed-point-algorithms
- 我最近写的一篇论文的源码，主要是对带参考信号的 ICA算法的扩展，多参考信号的ICA固定点算法。-I recently wrote a paper source, mainly for reference signal with the ICA algorithm expansion more reference signal ICA fixed-point algorithms.
duocankaoxinhaonewicar
- 多参考信号的新ICA，处于fastica与ica-r之间的一种新ica方法。-Multi-reference signal, the new ICA, in the fastica of ica-r a new ica method.
CMU-CS
- 很好的ica算法，计算ica时候用到，很好参考价值，卡内基学院用的程序-Good ica algorithm to calculate ica when used, a good reference value, the Carnegie Institute program
ica_D_R
- 具有参考信号的独立分量分析程序，国外的提出这个问题的人编写的。-Independent component analysis (ICA) program with a reference signal abroad website.
ICA(1)
- ICA独立分量图像特征提取,内含源程序和图片，程序完整、易懂，有很好的参考价值。-The ICA independent component image feature extraction，Contains the source program and pictures, complete, and easy to understand and have a very good reference value.
visualization.tar
- 相比DeconvNet写得比较简洁易懂。但是原来代码里面给的数据似乎没法跑。所幸同作者还有一些代码用到了TCNN，比如action recognition，可以一起下载下来参考。这个代码主要特色就是tiled结构，可以用来参考，然后把里面ICA的优化函数换成RICA，Sparse Coding等等。总之，在这个代码里也了解了不少(比如line search等)。-DeconvNet well written and easy to understand compared to relativel
icml09-deepbeliefnetwork
- 相比DeconvNet写得比较简洁易懂。但是原来代码里面给的数据似乎没法跑。所幸同作者还有一些代码用到了TCNN，比如action recognition，可以一起下载下来参考。这个代码主要特色就是tiled结构，可以用来参考，然后把里面ICA的优化函数换成RICA，Sparse Coding等等。总之，在这个代码里也了解了不少(比如line search等)。-DeconvNet well written and easy to understand compared to relativel
eeglab10.2.2.4b
- This contains original Matlab functions the EEGLAB (formerly ICA/EEG) Matlab toolbox, all released under the Gnu public license (see eeglablicence.txt). See the EEGLAB tutorial and reference paper (URLs given below) for more information. -This
filterECG
- this file proposes the use of independent component analysis (ICA) for removing ECG contamination EMG and compared it with other procedures previously developed to decontaminate EMG. To mimic realistic contamination while having uncontaminated refe
removal-artifacts--EEG-
- 通过小波分析和独立分量分析来去除脑电信号中的运动伪迹，有很好的参考价值。-Wavelet analysis and independent component analysis to remove EEG motion artifact, a good reference value.(Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and
SVM--ICA-and-PCA-and-NN
- SVM,ICA,PCA,NN等等模式识别算法,很有参考-SVM, ICA and PCA and NN, and so on pattern recognition algorithm, is of great reference value