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Irish Conference on Artificial Intelligence and Cognitive Science

AICS 2002: Artificial Intelligence and Cognitive Science pp 150–157Cite as

Identification of Visual Features Using a Neural Version of Exploratory Projection Pursuit

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2464))

Abstract

We develop artificial neural networks which extract structure from visual data. We explore an extension of Hebbian Learning which has been called ɛ- Insensitive Hebbian Learning and show that it may be thought of as a special case of Maximum Likelihood Hebbian learning and investigate the resulting network with both real and artificial data. We show that the resulting network is able to identify a single orientation of bars from a mixture of horizontal and vertical bars and also it is able to identify local filters from video images.

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References

  1. H.B. Barlow,. and, D.J. Tolhurst. Why Do You Have Edge Detectors, Optical Society of America, Technical Digest, 23, 171. (1992).

    Google Scholar 

  2. C.M. Bishop. Neural Networks for Pattern Recognition, Oxford, 1995.

    Google Scholar 

  3. D. Charles, and C. Fyfe. Modelling Multiple Cause Structure Using Rectification Constraints. Network: Computation in Neural Systems, 9:167–182, 1998.

    Article  MATH  Google Scholar 

  4. D. Charles, and C. Fyfe, Rectified Gaussian Distribution and the Identification of Multiple Causes Structure in Data. ICANN 99, 1999.

    Google Scholar 

  5. E. Corchado, D. MacDonald and C. Fyfe, Maximum and Minimum Likelihood Hebbian Learning. Data Mining and Knowledge Discover. Submitted. 2002.

    Google Scholar 

  6. D.J. Field, What is the goal of sensory coding? Neural Computation 6, 559–60, 1994.

    Article  Google Scholar 

  7. P. Földiák, Models of Sensory Coding PhD thesis, University of Cambridge, 1992.

    Google Scholar 

  8. J. Friedman, and J. Tukey, A Projection Pursuit Algorithm for Exploratory Data Analysis. IEEE Transaction on Computers, (23): 881–890, 1974.

    Article  MATH  Google Scholar 

  9. C. Fyfe, “PCA Properties of Interneurons”, From Neurobiology to Real World Computing, Proceedings of International Conference on Artificial on Artificial Neural Networks, ICAAN 93, pages 183–188, 1993.

    Google Scholar 

  10. C. Fyfe, Negative Feedback as an Organising Principle for Artificial Neural Networks, PhD Thesis, Strathclyde University, 1995.

    Google Scholar 

  11. C. Fyfe, and R. Baddeley, Non-linear data structure extraction using simple Hebbian networks, Biological Cybernetics 72(6), p533–541, 1995.

    Article  MATH  Google Scholar 

  12. C. Fyfe, A Scale Invariant Map. Network: Computing in Neural Systems, 7: pp 269–275, 1996.

    Article  Google Scholar 

  13. C. Fyfe, A Neural Network for PCA and Beyond, Neural Processing Letters, 6:33–41, 1997.

    Article  Google Scholar 

  14. D. Fyfe and C. Charles. Using Noise to Form a Minimal Overcomplete Basis, ICANN 99. 1999.

    Google Scholar 

  15. C. Fyfe and E. Corchado. Maximum Likelihood Hebbian Rules. European Symposium on Artificial Neural Networks. Esann 2002, 2002.

    Google Scholar 

  16. C. Fyfe, and D. MacDonald, ɛ-Insensitive Hebbian learning, Neuro Computing, 2001.

    Google Scholar 

  17. Y. Han, and C. Fyfe, A General Class of Neural Networks for P.C.A and F.A. Pages 158–163. Intelligent Data Engineering and Automated Learning. IDEAL 2000, 2000.

    Google Scholar 

  18. A. Hyvärinen, Complexity Pursuit: Separating interesting components from time series. Neural Computation, 13: 883–898, 2001.

    Article  MATH  Google Scholar 

  19. J. Karhunen, and J. Joutsensalo, Representation and Separation of Signals Using Nonlinear PCA Type Learning, Neural Networks, 7:113–127, 1994.

    Article  Google Scholar 

  20. E. Oja, Neural Networks, Principal Components and Subspaces, International Journal of Neural Systems, 1:61–68, 1989.

    Article  MathSciNet  Google Scholar 

  21. A.J. Smola, and. B. Scholkopf, A Tutorial on Support Vector Regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series. 1998.

    Google Scholar 

  22. J.H. Van Hateren, and A. Van der Schaaf, Independent Component Filters of Natural Images Compared with Simple Cells in Primary Visual Cortex. Proceedings of The Royal Society. London. B 265, 359–366, 1998.

    Google Scholar 

  23. L Xu. “Least Mean Square Error Reconstruction for Self-Organizing Nets”, Neural Networks, Vol. 6, pp. 627–648, 1993.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Area de Lenguajes y Sist. Informáticos. Departamento de Ingenieria Civil, Universidad de Burgos, Spain

    Emilio Corchado

  2. Applied Computational Intelligence Research Unit, The University of Paisley, Scotland

    Colin Fyfe

Authors
  1. Emilio Corchado
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  2. Colin Fyfe
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Ireland

    Michael O’Neill , Richard F. E. Sutcliffe , Conor Ryan , Malachy Eaton  & Niall J. L. Griffith , , ,  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Corchado, E., Fyfe, C. (2002). Identification of Visual Features Using a Neural Version of Exploratory Projection Pursuit. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_19

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  • DOI: https://doi.org/10.1007/3-540-45750-X_19

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44184-7

  • Online ISBN: 978-3-540-45750-3

  • eBook Packages: Springer Book Archive

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