Applying category theory to improve the performance of a neural architecture

Authors:
Michael J. Healy;Richard D. Olinger;Robert J. Young;Shawn E. Taylor;Thomas Caudell;Kurt W. Larson
Affiliations:
Department of Electrical and Computer Engineering, University of New Mexico, USA;Department of Electrical and Computer Engineering, University of New Mexico, USA;Department of Computer Science, University of New Mexico, USA;Department of Electrical and Computer Engineering, University of New Mexico, USA;Department of Electrical and Computer Engineering, University of New Mexico, USA and Department of Computer Science, University of New Mexico, USA;Sandia National Laboratories, Albuquerque, New Mexico, USA
Venue:
Neurocomputing
Year:
2009

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Abstract

A recently developed mathematical semantic theory explains the relationship between knowledge and its representation in connectionist systems. The semantic theory is based upon category theory, the mathematical theory of structure. A product of its explanatory capability is a set of principles to guide the design of future neural architectures and enhancements to existing designs. We claim that this mathematical semantic approach to network design is an effective basis for advancing the state of the art. We offer two experiments to support this claim. One of these involves multispectral imaging using data from a satellite camera.