The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - March (2002 vol.24)
pp: 301-312
ABSTRACT
<p>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.</p>
INDEX TERMS
data mining, pattern recognition, dimensionality reduction, feature clustering, multiscale representation, entropy measures
CITATION
P. Mitra, C.A. Murthy, S.K. Pal, "Unsupervised Feature Selection Using Feature Similarity", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.24, no. 3, pp. 301-312, March 2002, doi:10.1109/34.990133
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool