JSM 2004 Online Program

Abstract #301665

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Activity Number:	218
Type:	Contributed
Date/Time:	Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor:	Section on Statistical Graphics
Abstract - #301665
Title:	A Mixture-based Approach to Latent Class Discovery
Author(s):	Jeffrey L. Solka*+
Companies:	Naval Surface Warfare Center Dahlgren Division
Address:	NSWCDD, Code B10, Dahlgren, VA, 22448-5100,
Keywords:	Gaussian ; mixtures ; latent ; class ; discriminant ; boundary
Abstract:	Given a set of pre-categorized n-dimensional observations, one is often interested in discovering hidden categories within the pre-defined categories. This paper will present new work that takes a Gaussian-mixtures-based approach to the problem. This approach uses Gaussian mixture models to subcategorize observations based on their relationship to the mixture-induced descriminant boundary. The methodology will be illustrated using artificial and real world datasets. Real-world datasets will include examples from the gene expression analysis arena and from the text-based classification arena.

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