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The Cluster Expansion: A Hierarchical Density Model

  • Stephen P Luttrell
Conference paper
Part of the Fundamental Theories of Physics book series (FTPH, volume 70)

Abstract

Density modelling in high-dimensional spaces is a difficult problem. In this paper a new model, called the cluster expansion, is proposed and discussed. The cluster expansion scales well to high-dimensional spaces, and it allows the integrals over model parameters that arise in Bayesian predictive distributions to be evaluated explicitly.

Keywords

Input Vector Input Space Predictive Distribution Dirichlet Distribution Cluster Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. [1]
    S. P. Luttrell, “The use of Bayesian and entropie methods in neural network theory”, Maximum Entropy and Bayesian Methods, ed. J. Skilling, Kluwer, pp: 363–370, 1989.Google Scholar
  2. [2]
    S. P. Luttrell, “A hierarchical network for clutter and texture modelling”, Proceedings of the SPIE Conference on Adaptive Signal Processing, ed. S. Haykin, San Diego, Vol. 1565, pp: 518–628, 1991.Google Scholar
  3. [3]
    S. P. Luttrell, “Adaptive Bayesian networks”, Proceedings of the SPIE Conference on Adaptive and Learning Systems, ed. F. A. Sadjadi, Orlando, Vol. 1706, pp: 140–151, 1992.Google Scholar
  4. S. P. Luttrell, “A trainable texture anomaly detector using the Adaptive Cluster Expansion (ACE) method”, RSRE Memorandum, No. 4437, 1990.Google Scholar

Copyright information

© British Crown Copyright 1994

Authors and Affiliations

  • Stephen P Luttrell
    • 1
  1. 1.Defence Research AgencyWorcestershireUK

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