2010 UnsupervisedFeatureSelectionfor

• (Cai et al., 2010) ⇒ Deng Cai, Chiyuan Zhang, and Xiaofei He. (2010). “Unsupervised Feature Selection for Multi-cluster Data.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835848

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• http://scholar.google.com/scholar?q=%22Unsupervised+feature+selection+for+multi-cluster+data%22+2010
• http://portal.acm.org/citation.cfm?id=1835848&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 UnsupervisedFeatureSelectionfor	Deng Cai Chiyuan Zhang Xiaofei He			Unsupervised Feature Selection for Multi-cluster Data		KDD-2010 Proceedings		10.1145/1835804.1835848		2010