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12 April 2004 Invariant extreme physical information and fuzzy clustering
Ravi C. Venkatesan
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Proceedings Volume 5421, Intelligent Computing: Theory and Applications II; (2004) https://doi.org/10.1117/12.548156
Event: Defense and Security, 2004, Orlando, Florida, United States
Abstract
A principled formulation for knowledge acquisition from discrete data based on a continuum-free invariance preserving extension of the Extreme Physical Information (EPI) theory of Frieden is presented. A systematic invariance preserving methodology to formulate and minimize lattice EPI fuzzy clustering objective functions, and, determine the concomitant constraints is suggested. Equivalence between invariant EPI (IEPI) fuzzy clustering, described within a discrete time-independent Schrodinger-like framework, and robust Possibilistic c-Means (PcM) clustering is exemplified. The constraints are shown to be consistent with Heisenberg's uncertainty principle. Numerical examples for exemplary cases are provided for multiple potential wells, without a-priori knowledge of the number of clusters.
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Ravi C. Venkatesan "Invariant extreme physical information and fuzzy clustering", Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); https://doi.org/10.1117/12.548156
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KEYWORDS
Data acquisition
Information theory
Knowledge acquisition
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