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Proceedings Paper

Experimental results using a nonlinear extension of the minimum average correlation energy (MACE) filter
Author(s): John W. Fisher; Jose C. Principe
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Paper Abstract

The minimum average correlation energy filter (MACE) filter has been shown to have superior performance for rejecting out of class inputs in pattern recognition applications. The MACE filter exhibits a sharp correlation peak at a specified location in the output plane and low correlation energy elsewhere. It has also been shown that the MACE filter suffers from poor generalization. Increasing the number of exemplars used to compute the filter coefficients can improve the generalization, but the number of exemplars is restricted by the stability of the computation. We show a simple extension of the MACE filter to nonlinear processing techniques (i.e. nonlinear associative memories) which exhibits improved generalization and discrimination performance. The operating parameters of the proposed extension are difficult to compute analytically and adaptive learning methods are needed. Since the output of the MACE filter is optimized over the output plane any nonlinear extension of the MACE filter should encompass the output plane as well. In general this leads to exhaustive training over the entire output plane over all training exemplars. We present an efficient method for computing the parameters of the nonlinear extension which greatly reduces the training iterations required. Experimental results with 35 GHz inverse synthetic aperture radar (ISAR) data are also shown.

Paper Details

Date Published: 28 March 1995
PDF: 12 pages
Proc. SPIE 2490, Optical Pattern Recognition VI, (28 March 1995); doi: 10.1117/12.205796
Show Author Affiliations
John W. Fisher, Univ. of Florida (United States)
Jose C. Principe, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 2490:
Optical Pattern Recognition VI
David P. Casasent; Tien-Hsin Chao, Editor(s)

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