图 书 馆
多集群数据的无监督特征选择Unsupervised Feature Selection for Multi-Cluster Data |
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| 课程网址: | http://videolectures.net/kdd2010_cai_ufsm/ |
| 主讲教师: | Deng Cai |
| 开课单位: | 浙江大学 |
| 开课时间: | 2010-10-01 |
| 课程语种: | 汉简 |
| 中文简介: |  |
| 课程简介: | In 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. In this paper, we consider the feature selection problem in unsupervised learning scenario, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. Traditional unsupervised 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 in this paper a new approach, called {\em Multi-Cluster Feature Selection} (MCFS), for unsupervised feature selection. Specifically, we select those features such that the multi-cluster 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. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithm. |
| 关 键 词: | 数据分析; 特征选择; 无监督学习; 组合优化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-21：lxf |
| 阅读次数: | 172 |