Minimum Cross-Entropy Approximation for Modeling of Highly Intertwining Data Sets at Subclass Levels

Authors:
Qiuming Zhu
Affiliations:
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182-0050. E-mail: zhuq@unomaha.edu
Venue:
Journal of Intelligent Information Systems
Year:
1998

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Abstract

We study the problem of how to accurately model the data sets that contain a number of highly intertwining sets in terms of their spatial distributions. Applying the Minimum Cross-Entropy minimizationtechnique, the data sets are placed into a minimum number of subclass clusters according to their high intraclass and low interclass similarities. The method leads to a derivation of the probability density functions for the data sets at the subclass levels. These functions then, in combination, serve as anapproximation to the underlying functions that describe the statistical features of each data set.