PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Spectral Clustering and Feature Selection for Microarray Data
Darío García-García and Raúl Santos-Rodríguez
In: International Conference on Machine Learning and Applications, 13-15 Dec 2009, Miami Beach.


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 spectralbased 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.

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EPrint Type:Conference or Workshop Item (Oral)
Additional Information:This paper won the ICMLA'09 Challenge on Functional Gene Clustering.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5596
Deposited By:Darío García-García
Deposited On:08 March 2010