特征选择
计算机科学
人工智能
聚类分析
选择(遗传算法)
模式识别(心理学)
特征学习
机器学习
星团(航天器)
无监督学习
数据挖掘
特征(语言学)
最小冗余特征选择
高维数据聚类
语言学
哲学
作者
Deng Cai,Chiyuan Zhang,Xiaofei He
出处
期刊:Knowledge Discovery and Data Mining
日期:2010-07-25
被引量:741
标识
DOI:10.1145/1835804.1835848
摘要
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 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.
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