Spectral clustering and feature selection for microarray data

Darío García-García*, Raúl Santos-Rodríguez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)

Abstract

Microarray datasets comprise a large number of gene expression values and a relatively small number of samples. Feature selection algorithms are very useful in these situations in order to find a compact subset of informative features. We propose a redundancy control method for algorithms in the recently proposed SPEC family of spectral-based feature selection algorithms. This method is applied to find relevant genes in order to cluster samples corresponding to three kinds of cancer: lung, breast and colon.

Original languageEnglish
Title of host publication8th International Conference on Machine Learning and Applications, ICMLA 2009
Pages425-428
Number of pages4
DOIs
Publication statusPublished - 2009
Event8th International Conference on Machine Learning and Applications, ICMLA 2009 - Miami Beach, FL, United States
Duration: 13 Dec 200915 Dec 2009

Publication series

Name8th International Conference on Machine Learning and Applications, ICMLA 2009

Conference

Conference8th International Conference on Machine Learning and Applications, ICMLA 2009
Country/TerritoryUnited States
CityMiami Beach, FL
Period13/12/0915/12/09

Bibliographical note

Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.

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