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

Abstract Details

Activity Number: 189
Type: Invited
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #308242
Title: Classfication Using Matrix Predictors with Application in Toxicant Identification
Author(s): Wenxuan Zhong*+
Companies: University of Illinois
Address: 725 South Wright Street, Champaign, IL, 61820,
Keywords: classification ; variable selection ; dimension reduction ; penalization ; discriminant analysis ; sensor array

Classification for high dimensional data is very important in modern statistical methodology development. In this article, we propose a penalized matrix classification method (PMCA), which can be viewed as a generalization of the conventional LDA method to the data with matrix predictors. To incorporate the predictors sparsity into the classification analysis, we regularized the $L_1$ norm of the matrix classifiers and integrate the classification and feature selection together. We studied asymptotic properties of the algorithm by both theoretical analyses and empirical examples, and showed its superior performances in comparison with existing methods by simulations. We also show the practical usage of PMCA on the classification of the volatile chemical toxicants.

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