资源简介
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
代码片段和文件信息
相关资源
- CSP共空间模式分解算法及特征值选取
- FREAK Matlab实现
- Feature similarity 计算方法的源代码
- 解决 错误使用 mcc Test checkout of featu
- image-texture-features 图像纹理特征提取
- Extract-Sound-Feature 利用matlab提取声音的
-
Rough-Set-ba
sed-Feature-Selection 离散人工 - adaboost-train-test 级联分类器学习
- hog-feature HOG(方向梯度直方图)图像
- LGBP-feature 提取Gabor特征
- Extract_Feature 包含PLP特征提取
- Facial-feature-detection 程序实现人脸特征
- 37724099hsvfeature1 基于视觉特征的图像特
-
NCC-and-the-SSDA-ba
sed-feature-points-match - MRFFeature
- feature-selection-svm 特征选择算法
- sift sift算法图像特征提取
- FCBF FCBF的matlab程序。FCBF是比较实用的
- time-frequency-feature 此代码用于故障诊断
- ICA特征提取
评论
共有 条评论