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Unsupervised Classifiers, Mutual Information and 'Phantom Targets'

Bridle, John S. and Heading, Anthony J. R. and MacKay, David J. C. (1992) Unsupervised Classifiers, Mutual Information and 'Phantom Targets'. In: Advances in Neural Information Processing Systems (NIPS 4). Advances in Neural Information Processing. No.4. Morgan Kaufmann , San Mateo, CA, pp. 1096-1101. ISBN 1-55860-222-4. https://resolver.caltech.edu/CaltechAUTHORS:20160119-164045651

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Abstract

We derive criteria for training adaptive classifier networks to perform unsupervised data analysis. The first criterion turns a simple Gaussian classifier into a simple Gaussian mixture analyser. The second criterion, which is much more generally applicable, is based on mutual information. It simplifies to an intuitively reasonable difference between two entropy functions, one encouraging 'decisiveness,' the other 'fairness' to the alternative interpretations of the input. This 'firm but fair' criterion can be applied to any network that produces probability-type outputs, but it does not necessarily lead to useful behavior.


Item Type:Book Section
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http://papers.nips.cc/paper/440-unsupervised-classifiers-mutual-information-and-phantom-targetsOrganizationArticle
Additional Information:Copyright © Controller HMSO London 1992.
Series Name:Advances in Neural Information Processing
Issue or Number:4
Record Number:CaltechAUTHORS:20160119-164045651
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160119-164045651
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:63784
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:20 Jan 2016 00:46
Last Modified:03 Oct 2019 09:31

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