机器学习的利器！ 特征选择的法宝！ kaggle 必备书！ -----Shi Long

高维数据 范数的支持向量机特征选择,搞算法的同学们可以好好看看

特征工程-特征选择思维导图：主要从常见搜索算法以及经典三刀来展示。这个是自己归纳的，有什么不对的，欢迎指出来

用粒子群优化算法自动优选特征组合，提高分类精度，减少运行时间

首先产生若干种群（特征子集），然后用PSO 算法对特征及参数进行优化。在UCI 标准数据集上进行的仿真实验表明，该算法可有效地找出合适的特征子集及LS-SVM 参数，且与基于遗传算法的最小二乘支持向量机算法（GALS-SVM）和传统的LS-SVM 算法相比具有较好的分类效果。

特征选择是机器学习的一个重要领域，改代码是经典无监督特征选择算法LaplacianScore算法matlab代码，这里上传给大家下载，希望能对大家有所帮助

特征选择DF方法实现源代码 要求要先自行分好词 代码中有详细注释

特征选择MATLAB，用于对高维特征进行降唯，深度学习也可用

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

基于特征的互信息计算,互信息配准的MATLAB程序代码！