Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Unsupervised feature selection with feature clustering

Language

English

Abstract

As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that the features in the same cluster contain similar structural information of the given instances. Subsequently, since the obtained feature subset consists of features from variant clusters, the similarity between selected features will be low. This allows us to reserve the most data structural information with the minimum number of features. Experimental results on different benchmark data sets demonstrate the superiority of the proposed method. © 2012 IEEE.

Keywords

Feature Clustering, Feature Redundancy, High-dimensional Data, Number of Features, Unsupervised Feature Selection

Publication Date

2012

Source Publication Title

Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops

Start Page

9

End Page

15

Conference Location

Macau, China

Publisher

IEEE

DOI

10.1109/WI-IAT.2012.259

Link to Publisher's Edition

http://dx.doi.org/10.1109/WI-IAT.2012.259

ISBN (print)

9781467360579

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