Unsupervised feature selection using feature similarity

In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature...

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Bibliographic Details
Published inIEEE transactions on pattern analysis and machine intelligence Vol. 24; no. 3; pp. 301 - 312
Main Authors Mitra, P., Murthy, C.A., Pal, S.K.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.03.2002
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Compressing
Entropy
Loss measurement
Pattern analysis
Redundancy
Representations
Similarity
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Summary:In this article, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The method is based on measuring similarity between features whereby redundancy therein is removed. This does not need any search and, therefore, is fast. A new feature similarity measure, called maximum information compression index, is introduced. The algorithm is generic in nature and has the capability of multiscale representation of data sets. The superiority of the algorithm, in terms of speed and performance, is established extensively over various real-life data sets of different sizes and dimensions. It is also demonstrated how redundancy and information loss in feature selection can be quantified with an entropy measure.
Bibliography:ObjectType-Article-2
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ISSN:0162-8828
1939-3539
DOI:10.1109/34.990133