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Representation theory and invariant neural networks

Representation theory and invariant neural networks
Representation theory and invariant neural networks
A feedforward neural network is a computational device used for pattern recognition. In many recognition problems, certain transformations exist which, when applied to a pattern, leave its classification unchanged. Invariance under a given group of transformations is therefore typically a desirable property of pattern classifiers. In this paper, we present a methodology, based on representation theory, for the construction of a neural network invariant under any given finite linear group. Such networks show improved generalisation abilities and may also learn faster than corresponding networks without inbuilt invariance. We hope in the future to generalise this theory to approximate invariance under continuous groups.
0166-218X
33-60
Wood, J.
65587872-7126-469a-851a-d60195d39058
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db
Wood, J.
65587872-7126-469a-851a-d60195d39058
Shawe-Taylor, J.
c32d0ee4-b422-491f-8c28-78663851d6db

Wood, J. and Shawe-Taylor, J. (1996) Representation theory and invariant neural networks. Discrete Applied Mathematics, 69 (1-2), 33-60. (doi:10.1016/0166-218X(95)00075-3).

Record type: Article

Abstract

A feedforward neural network is a computational device used for pattern recognition. In many recognition problems, certain transformations exist which, when applied to a pattern, leave its classification unchanged. Invariance under a given group of transformations is therefore typically a desirable property of pattern classifiers. In this paper, we present a methodology, based on representation theory, for the construction of a neural network invariant under any given finite linear group. Such networks show improved generalisation abilities and may also learn faster than corresponding networks without inbuilt invariance. We hope in the future to generalise this theory to approximate invariance under continuous groups.

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Published date: August 1996
Organisations: Electronics & Computer Science

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Local EPrints ID: 250474
URI: http://eprints.soton.ac.uk/id/eprint/250474
ISSN: 0166-218X
PURE UUID: 80f3abb8-efe1-4d19-b0ae-703ad4aebb91

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Date deposited: 01 Jun 1999
Last modified: 14 Mar 2024 04:52

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Contributors

Author: J. Wood
Author: J. Shawe-Taylor

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