Stack Filter Classifiers

Authors:
Reid Porter;G. Beate Zimmer;Don Hush
Affiliations:
Los Alamos National Laboratory, Los Alamos 87544;Department of Mathematics and Statistics, Texas A&M University --- Corpus Christi, Corpus Christi 78412-5825;Los Alamos National Laboratory, Los Alamos 87544
Venue:
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Year:
2009

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Abstract

Stack Filters define a large class of increasing filter that is used used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: 1) fast and efficient implementation, 2) the relationship to mathematical morphology and 3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in [1]. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help control estimation errors, and also suggests new learning algorithms for Boolean function classifiers when they are applied to real-valued inputs.