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.
|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|
|Deposited By:||Darío García-García|
|Deposited On:||08 March 2010|