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Proceedings Paper

Unsupervised learning for nonlinear synthetic discriminant functions
Author(s): John W. Fisher; Jose C. Principe
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Paper Abstract

It has been shown in previous work that the family of filters which includes the minimum average correlation energy (MACE) filter can be formulated as a linear associative memory (LAM) preceded by a linear pre-processor which changes depending on the optimization criterion. We have presented a methodology by which the MACE filter and other synthetic discriminant function (SDF) filters can be extended to nonlinear processing structures (i.e. nonlinear associative memories) resulting in improved performance with respect to generalization and out-of-class target rejection. Our earlier focus was towards developing efficient training algorithms for computing a nonlinear discriminant function without changing the linear pre-processor. In this paper we discuss a nonlinear pre-processing method based on concepts of information theory. We show a simple unsupervised method by which input images can be nonlinearly transformed onto a maximum entropy feature space.

Paper Details

Date Published: 15 March 1996
PDF: 12 pages
Proc. SPIE 2752, Optical Pattern Recognition VII, (15 March 1996); doi: 10.1117/12.235636
Show Author Affiliations
John W. Fisher, Univ. of Florida (United States)
Jose C. Principe, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 2752:
Optical Pattern Recognition VII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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