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Proceedings Paper

Invariant extreme physical information and fuzzy clustering
Author(s): Ravi C. Venkatesan
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Paper 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.

Paper Details

Date Published: 12 April 2004
PDF: 10 pages
Proc. SPIE 5421, Intelligent Computing: Theory and Applications II, (12 April 2004); doi: 10.1117/12.548156
Show Author Affiliations
Ravi C. Venkatesan, Systems Research Corp. (India)


Published in SPIE Proceedings Vol. 5421:
Intelligent Computing: Theory and Applications II
Kevin L. Priddy, Editor(s)

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