Unsupervised feature selection with feature clustering

Yiu Ming CHEUNG, Hong Jia

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Pages9-15
Number of pages7
DOIs
Publication statusPublished - 2012
Event2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China
Duration: 4 Dec 20127 Dec 2012

Publication series

NameProceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012

Conference

Conference2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Country/TerritoryChina
CityMacau
Period4/12/127/12/12

Scopus Subject Areas

  • Artificial Intelligence
  • Software

User-Defined Keywords

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

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