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 - #307237
Title: An Efficient Method of Estimating the Bayesian Classifier in Signal Detection Tasks Involving Complex High-Dimensional Data
Author(s): Subok Park*+ and Eric Clarkson
Companies: FDA and The University of Arizona
Address: , , 20993,
Keywords: Markov-chain Monte Carlo ; Likelihood ratio ; Non-Gaussian statistics ; High-dimensional data ; Feature selection ; Bayesian optimal classifier

The Bayesian classifier, which uses the likelihood ratio as a test statistic, optimizes many figures-of-merit for signal detection tasks. Due to the high dimensionality and complexity of the data arising in many realistic applications, the probability density functions required for this classifier are often unknown, leaving the task of calculating the classifier's performance infeasible. With appropriate feature selection methods, data reduction can be used to approximate the performance of this classifier. In essence, a Markov-chain Monte Carlo method is used to calculate the likelihood ratio of the reduced data, and hence to approximate the unconstrained classifier. We demonstrate this method in detection tasks involving images with random backgrounds that follow a non-Gaussian distribution. Singular value decomposition and partial least squares are employed for feature selection.

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