2014a Test checkout of featur compiler failed，如果报错Test checkout of feature 'Compiler' failed ，是因为你的matlab2014a破解不完全。下载文件替换相应的文件即可（install.jar ，compiler.dll，mcc.exe，libmwservices.dll）license.lic改为与MATLAB\licenses文件夹下的那个lic文件同名，复制并替换之

python实现特征提取深度学习，最详细的代码讲解，欢迎大家多多交流。

本资源是matlab特征选择的特征选择函数库，包含大量的特征选择所需的源码，包括 relieff， ILFS等，需要的可以下载，此版本带有license。

feature extraction深度学习的实现，代码内容详细，有不懂之处欢迎大家多多交流

光谱的变量选择/特征选择算法

The DEMO includes 5 feature selection algorithms: • Sequential Forward Selection (SFS) • Sequential Floating Forward Selection (SFFS) • Sequential Backward Selection (SBS) • Sequential Floating Backward Selection (SFBS) • ReliefF

n many data analysis tasks, one is often confronted with very high dimensional data. Feature selection techniques are designed to find the relevant feature subset of the original features which can facilitate clustering, classification and retrieval. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. Traditional feature selection methods address this issue by selecting the top ranked features based on certain scores computed independently for each feature. These approaches neglect the possible correlation between different features and thus can not produce an optimal feature subset. Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, we propose here a new approach, called {\em Multi-Cluster/Class Feature Selection} (MCFS), for feature selection. Specifically, we select those features such that the multi-cluster/class structure of the data can be best preserved. The corresponding optimization problem can be efficiently solved since it only involves a sparse eigen-problem and a L1-regularized least squares problem. It is important to note that MCFS can be applied in superised, unsupervised and semi-supervised cases. If you find these algoirthms useful, we appreciate it very much if you can cite our following works: Papers Deng Cai, Chiyuan Zhang, Xiaofei He, "Unsupervised Feature Selection for Multi-cluster Data", 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10), July 2010. Bibtex source Xiaofei He, Deng Cai, and Partha Niyogi, "Laplacian Score for Feature Selection", Advances in Neural Information Processing Systems 18 (NIPS'05), Vancouver, Canada, 2005 Bibtex source