This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 519
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #309273
Title: Variable Selection for Gene Expression Data via Hilbert-Schmidt Independence Criterion
Author(s): Ali Ghodsi*+
Companies: University of Waterloo
Address: , , ON, N2L 3G1, Canada
Keywords: variable seclection ; Gene selection ; Classification ; Clustering ; Sparse Decomposition
Abstract:

A novel feature selection technique for microarray gene expression data is proposed. It is based on the Hilbert- Schmidt independence criterion, and partly motivated by Rank-One Downdate (R1D) and the Singular Value Decomposition (SVD). The algorithm selects a small set of genes such that the response variable depends mainly on this subset, at the exclusion of the rest of the genes. The algorithm is computationally very fast and scalable to large data sets, and it does not require the number of important genes as an explicit input. Experimental results of the proposed technique are presented on some synthetic and well-known microarray data sets.


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