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
CountryUnited States
CityMiami Beach, FL
Period13/12/0915/12/09

Bibliographical note

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

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