Simultaneous feature selection and clustering using mixture models
Law, M.H.C.
Figueiredo, M.A.T.
Jain, A.K.
Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sept. 2004
Volume: 26,
Issue: 9
On page(s): 1154-1166
ISSN: 0162-8828
INSPEC Accession Number: 8102117
Digital Object Identifier: 10.1109/TPAMI.2004.71
Current Version Published: 2004-07-26
Abstract
Clustering is a common unsupervised learning technique used to discover group structure in a set of data. While there exist many algorithms for clustering, the important issue of feature selection, that is, what attributes of the data should be used by the clustering algorithms, is rarely touched upon. Feature selection for clustering is difficult because, unlike in supervised learning, there are no class labels for the data and, thus, no obvious criteria to guide the search. Another important problem in clustering is the determination of the number of clusters, which clearly impacts and is influenced by the feature selection issue. In this paper, we propose the concept of feature saliency and introduce an expectation-maximization (EM) algorithm to estimate it, in the context of mixture-based clustering. Due to the introduction of a minimum message length model selection criterion, the saliency of irrelevant features is driven toward zero, which corresponds to performing feature selection. The criterion and algorithm are then extended to simultaneously estimate the feature saliencies and the number of clusters.
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