
Accession Number : ADA299178
Title : Derivation of Joint Representation Mixture Model Equations,
Corporate Author : NAVAL SURFACE WARFARE CENTER DAHLGREN DIV VA
Personal Author(s) : Rogers, George W. ; Lorey, Richard
PDF Url : ADA299178
Report Date : MAY 1995
Pagination or Media Count : 21
Abstract : One of the problems that arises in many largescale applications of mixture models to density estimation is that, as the size of the data set increases, the class labeled data becomes a (proper) subset of the total data set. That is, while many small data sets may have all the observations labeled as to class membership, large data sets often consist of labeled subsets plus a potentially large unlabeled subset. Thus, it is desirable to have a unified framework for handling this combined supervised (class labeled data)/unsupervised (unlabeled data) problem. This is the motivation behind the following development of joint representation mixture models. The joint representation mixture model is defined, likelihood functions corresponding to different levels of data categorization with respect to class are presented, and the resultant iterative ExpectationMaximization equations are derived. (AN)
Descriptors : *MATHEMATICAL MODELS, *PROBABILITY DENSITY FUNCTIONS, DATA BASES, ALGORITHMS, PARAMETRIC ANALYSIS, MAXIMUM LIKELIHOOD ESTIMATION, MULTIVARIATE ANALYSIS, STATISTICAL DATA, APPROXIMATION(MATHEMATICS), LOGARITHM FUNCTIONS, NORMAL DISTRIBUTION.
Subject Categories : Statistics and Probability
Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE