Feature Selection and Gene Clustering from Gene Expression Data

In this article we describe an algorithm for feature selection and gene clustering from high dimensional gene expression data. The method is based on measuring similarity between features/genes whereby redundancy therein is removed. This does not need any search and therefore is fast. A novel feature similarity measure, called maximum information compression index, is used. The feature selection algorithm also obtains gene clusters in a multiscale fashion. The superiority of the algorithm, in terms of speed and performance, is established on a real life molecular cancer classification dataset.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: D. Dutta Majumder, Pabitra Mitra
• Keywords: Computer Vision, Dimensional Gene Expression, Feature Selection Algorithm, Feature Similarity Measure, ICPR 2004