Consensus unsupervised feature ranking from multiple views

Feature ranking is a kind of feature selection process which ranks the features based on their relevances and importance with respect to the problem. This topic has been well studied in supervised classification area. However, very few works are done for unsupervised clustering under the condition t...

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Bibliographic Details
Published inPattern recognition letters Vol. 29; no. 5; pp. 595 - 602
Main Authors Hong, Yi, Kwong, Sam, Chang, Yuchou, Ren, Qingsheng
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.04.2008
Elsevier
Subjects
Applied sciences
Clustering
Exact sciences and technology
Feature ranking ensembles
Information, signal and communications theory
Miscellaneous
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Unsupervised feature selection
Clustering
Feature ranking ensembles
Unsupervised feature selection
Automatic classification
Signal processing
Feature extraction
Unsupervised classification
Signal classification
Supervised classification
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Summary:Feature ranking is a kind of feature selection process which ranks the features based on their relevances and importance with respect to the problem. This topic has been well studied in supervised classification area. However, very few works are done for unsupervised clustering under the condition that labels of all instances are unknown beforehand. Thus, feature ranking for unsupervised clustering is a challenging task due to the absence of labels of instances for guiding the computations of the relevances of features. This paper explores the feature ranking approach within the unsupervised clustering area. We propose a novel consensus unsupervised feature ranking approach, termed as unsupervised feature ranking from multiple views (FRMV). The FRMV method firstly obtains multiple rankings of all features from different views of the same data set and then aggregates all the obtained feature rankings into a single consensus one. Experimental results on several real data sets demonstrate that FRMV is often able to identify a better feature ranking when compared with that obtained by a single feature ranking approach.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2007.11.012