Applied Intelligence

, Volume 6, Issue 3, pp 185–203 | Cite as

Unsupervised neural network learning procedures for feature extraction and classification

  • Suzanna Becker
  • Mark Plumbley
Article

Abstract

In this article, we review unsupervised neural network learning procedures which can be applied to the task of preprocessing raw data to extract useful features for subsequent classification. The learning algorithms reviewed here are grouped into three sections: information-preserving methods, density estimation methods, and feature extraction methods. Each of these major sections concludes with a discussion of successful applications of the methods to real-world problems.

Keywords

unsupervised learning self-organization information theory feature extraction signal processing 

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References

  1. 1.
    H.M. Abbas and M.M. Fahmy, “A neural model for adaptive Karhunen Loéve transform (KLT),” in Proceedings of the International Joint Conference on Neural Networks, IJCNN-92, Baltimore, 1992, vol. II, pp. 975–980.Google Scholar
  2. 2.
    J.J. Atick and A.N. Redlich, “Predicting ganglion and simple cell receptive field organizations from information theory,” Institute for Advanced Study, Princeton, Technical Report, IASSNS-HEP-89/55, 1989.Google Scholar
  3. 3.
    J.J. Atick and A.N Redlich, “Towards a theory of early visual processing,” Neural Computation, vol. 2, pp. 308–320, 1990.Google Scholar
  4. 4.
    P. Baldi and K. Hornik, “Neural networks and principal component analysis: Learning from examples without local minima,” Neural Networks, vol. 2, pp. 53–58, 1989.Google Scholar
  5. 5.
    H.B. Barlow, “Unsupervised learning,” Neural Computation, vol. 1, pp. 295–311, 1989.Google Scholar
  6. 6.
    S. Becker, An Information-theoretic Unsupervised Learning Algorithm for Neural Networks, Ph.D. Thesis, University of Toronto, 1992.Google Scholar
  7. 7.
    S. Becker, “Learning to categorize objects using temporal coherence,” in Advances in Neural Information Processing Systems 5, Morgan Kaufmann, pp. 361–368, 1993.Google Scholar
  8. 8.
    S. Becker and G.E. Hinton, “A self-organizing neural network that discovers surfaces in random-dot stereograms,” Nature, vol. 355, pp. 161–163, 1992.Google Scholar
  9. 9.
    S. Becker and G.E. Hinton, “Learning mixture models of spatial coherence,” Neural Computation, vol. 5, no. 2, pp. 267–277, 1993.Google Scholar
  10. 10.
    A.J. Bell, “Self-organisation in real neurons: Anti-hebb in ‘channel space’,” in Advances in Neural Information Processing Systems 4, Morgan Kaufmann, pp. 59–66, 1992.Google Scholar
  11. 11.
    E.L. Bienenstock, L.N. Cooper, and P.W. Munro, “Theory for the development of neuron selectivity; orientation specificity and binocular interaction in visual cortex,” Journal of Neuroscience, vol. 2, pp. 32–48, 1982.Google Scholar
  12. 12.
    H. Bourlard and Y. Kamp, “Auto-association by multilayer perceptrons and singular value decomposition,” Biological Cybernetics, vol. 59, pp. 291–294, 1988.Google Scholar
  13. 13.
    J.S. Bridle, “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in NATO ASI Series on Systems and Computer Science, edited by F. Fougelman-Soulie and J. Herault, Springer-Verlag, 1990.Google Scholar
  14. 14.
    G.A. Carpenter and S. Grossberg, “A massively parallel architecture for a self-organizing neural pattern recognition machine,” Computer Vision, vol. 37, pp. 54–115, 1983.Google Scholar
  15. 15.
    G.W. Cottrell, P.W. Munro, and D. Zipser, “Image compression by back propagation: A demonstration of extensional programming,” in Advances in Cognitive Science, edited by N.E. Sharkey, vol. 2, Abbex: Norwood, NJ, 1989.Google Scholar
  16. 16.
    A.P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Proceedings of the Royal Statistical Society, vol. B 39, 1977, pp. 1–38.Google Scholar
  17. 17.
    J.L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, pp. 179–211, 1990.Google Scholar
  18. 18.
    J.L. Elman and D. Zipser, “Learning the hidden structure of speech,” Institute of Cognitive Science, University of California, San Diego, ICS Report 8701, 1987.Google Scholar
  19. 19.
    F. Fallside, “On the analysis of multi-dimensional linear predictive/autoregressive data by a class of single layer connectionist models,” in IEE Conference on Artificial Neural Networks, pp. 176–180, 1989.Google Scholar
  20. 20.
    P. Földiák, “Adaptive network for optimal linear feature extraction,” in Proceedings of the International Joint Conference on Neural Networks, IJCNN-89, Washington, DC, 1989, pp. 401–405.Google Scholar
  21. 21.
    Y. Freund and D. Haussler, “Unsupervised learning of distributions on binary vectors using 2-layer networks,” in Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers, pp. 912–919, 1992.Google Scholar
  22. 22.
    K. Fukushima, “Cognitron: A self-organizing multilayered neural network,” Biological Cybernetics, vol. 20, pp. 121–136, 1975.Google Scholar
  23. 23.
    K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biological Cybernetics, vol. 36, pp. 193–202, 1980.Google Scholar
  24. 24.
    K. Fukushima, “A hierarchical neural network model for associative memory,” Biological Cybernetics, vol. 50, pp. 105–113, 1984.Google Scholar
  25. 25.
    C. Galland, Learning in Deterministic Boltzmann Machine Networks, Ph.D. Thesis, University of Toronto, 1992.Google Scholar
  26. 26.
    J.J. Gerbrands, “On the relationships between SVD, KLT and PCA,” Pattern Recogntion, vol. 14, pp. 375–381, 1981.Google Scholar
  27. 27.
    G.H. Golub and C.F.Van Loan, Matrix Computations, North Oxford Academic: Oxford, 1983.Google Scholar
  28. 28.
    R.C. Gonzalez and P. Wintz, Digital Image Processing, Addison-Wesley: Reading, MA, second edition, 1987.Google Scholar
  29. 29.
    G.E. Hinton and T.J. Sejnowski, “Learning and relearning in Boltzmann machines,” in Parallel distributed processing: Explorations in the microstructure of cognition, edited by D.E. Rumelhart, J.L. McClelland, and the PDP research group, MIT Press: Cambridge, MA, vol. I, pp. 282–317, 1986.Google Scholar
  30. 30.
    K. Hornik and C.-M. Kuan, “Convergence analysis of local feature extraction algorithms,” Neural Networks, vol. 5, pp. 229–240, 1992.Google Scholar
  31. 31.
    N. Intrator, “Feature extraction using an unsupervised neural network,” Neural Computation, vol. 4, no. 1, pp. 98–107, 1992.Google Scholar
  32. 32.
    R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton, “Adaptive mixtures of local experts,” Neural Computation, vol. 3, no. 1, 1991.Google Scholar
  33. 33.
    M.I. Jordan and R.A. Jacobs, “Hierarchies of adaptive experts,” in Advances in Neural Information Processing Systems 5, Morgan Kaufmann, pp. 985–992, 1993.Google Scholar
  34. 34.
    C. Jutten and J. Herault, “Blind separation of sources, part I: An adaptive algorithm based on enuromimetic architecture,” Signal Processing, vol. 24, pp. 1–10, 1991.Google Scholar
  35. 35.
    C. Jutten and J. Herault, “Blind separation of sources, part II: Problems statement,” Signal Processing, vol. 24, pp. 11–20, 1991.Google Scholar
  36. 36.
    J. Karhunen and J. Joutsensalo, “Tracking of sinusoidal frequencies by neural network learning algorithms,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP-91, Toronto, Canada, 1991.Google Scholar
  37. 37.
    T. Kohonen, “Clustering, taxonomy, and topological maps of patterns,” in Proceedings of the Sixth International Conference on Pattern Recognition, edited by M. Lang, IEEE Computer Society Press: Silver Spring, MD, 1982.Google Scholar
  38. 38.
    T. Kohonen, “The ‘neural’ phonetic typewriter,” IEEE Computer, vol. 21, pp. 11–22, 1988.Google Scholar
  39. 39.
    T. Kohonen and E. Oja, “Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements,” Biological Cybernetics, vol. 21, pp. 85–95, 1976.Google Scholar
  40. 40.
    S.Y. Kung and K.I. Diamantaras, “A neural network learning algorithm for adaptive principal component extraction (APEX),” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing ICASSP-90, vol. II, 1990, pp. 861–864.Google Scholar
  41. 41.
    A.S. Lapedes and R.M. Farber, “Nonlinear signal processing using neural networks: Prediction and system modelling,” Los Alamos National Laboratory, Technical Report LA-UR-87-2662, 1987.Google Scholar
  42. 42.
    T.K. Leen, “Dynamics of learning in linear feature-discovery networks,” Network, vol. 2, pp. 85–105, 1991.Google Scholar
  43. 43.
    T.K. Leen, M. Rudnick, and D. Hammerstrom, “Hebbian feature discovery improves classifier efficiency,” in Proceedings of the International Joint Conference on Neural Networks, IJCNN-89, Washington, DC, 1989, pp. I: 51–56.Google Scholar
  44. 44.
    R. Linsker, “Self-organization in a perceptual network,” IEEE Computer, vol. 21, no. 3, pp. 105–117, March 1988.Google Scholar
  45. 45.
    R. Linsker, “Deriving receptive fields using an optimal encoding criterion,” in Advances in Neural Information Processing Systems 5, Morgan Kaufmann, pp. 953–960, 1993.Google Scholar
  46. 46.
    S.P. Luttrell, “Hierarchical vector quantisation,” in Proceedings of the Inst. of Elec. Eng., vol. 136, pp. 405–413, 1989.Google Scholar
  47. 47.
    M.C. Mozer, “Discovering discrete distributed representations with iterative competitive learning,” in Advances in Neural/Information Processing Systems 3, Morgan Kaufmann, pp. 627–634, 1991.Google Scholar
  48. 48.
    M.C. Mozer, “Induction of multiscale temporal structure,” in Advances in Neural Information Processing Systems 4, Morgan Kaufmann, pp. 275–282, 1992.Google Scholar
  49. 49.
    M.C. Mozer, “Neural net architectures for temporal sequence processing,” in Predicting the future and understanding the past, edited by A. Weigend and N. Gershenfeld, Addison-Wesley Publishing: Redwood City, CA, 1993.Google Scholar
  50. 50.
    R.M. Neal, “Connectionist learning of belief networks,” Artificial Intelligence, vol. 56, pp. 71–113, 1992.Google Scholar
  51. 51.
    R.M. Neal and G.E. Hinton, “A new view of the EM algorithm that justifies incremental and other variants,” Submitted for publication.Google Scholar
  52. 52.
    S.J. Nowlan, “Maximum likelihood competitive learning,” in Neural Information Processing Systems,edited by D.S. Touretzky, Morgan Kaufmann: San Mateo, CA, vol. 2, pp. 574–582, 1990.Google Scholar
  53. 53.
    S.J. Nowlan, Soft Competitive Adaptation: Neural Network Learning Algorithms based on Fitting Statistical Mixtures, Ph.D. Thesis, Carnegie-Mellon University, Pittsburgh PA, 1991. Also published as CMU Technical Report CMU-CS-91–126.Google Scholar
  54. 54.
    E. Oja, “A simplified neuron model as a principal component analyser,” Journal of Mathematical Biology, vol. 15, pp. 267–273, 1982.Google Scholar
  55. 55.
    E. Oja, “Neural networks, principal components, and subspaces,” International Journal of Neural Systems, vol. 1, no. 1, pp. 61–68, 1989.Google Scholar
  56. 56.
    E. Oja, “Principal components, minor components, and linear neural networks,” Neural Networks, vol. 5, pp. 927–935, 1992.Google Scholar
  57. 57.
    E. Oja and J. Karhunen, “On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix,” Journal of Mathematical Analysis and Applications, vol. 106, pp. 69–84, 1985.Google Scholar
  58. 58.
    E. Oja, H. Ogawa, and J. Wangviwattana, “PCA in fully parallel neural networks,” in Artificial Neural Networks, edited by I. Aleksander and J. Taylor, North-Holland: Amsterdam, vol. 2, pp. 199–202, 1992.Google Scholar
  59. 59.
    J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann: San Mateo, California, 1988.Google Scholar
  60. 60.
    B.A. Pearlmutter and G.E. Hinton, “G-maximization: An unsupervised learning procedure for discovering regularities,” in Neural Networks for Computing: American Institute of Physics Conference Proceedings 151, edited by J.S.Denker, pp. 333–338, 1986.Google Scholar
  61. 61.
    C. Peterson and J.R. Anderson, “A mean field theory learning algorithm for neural networks,” Complex Systems, vol. 1, pp. 995–1019, 1987.Google Scholar
  62. 62.
    C. Peterson and E. Hartman, “Explorations of the mean field theory learning algorithm,” Neural Networks, vol. 2, p. 475, 1989.Google Scholar
  63. 63.
    M.D. Plumbley, “Efficient information transfer and anti-Hebbian neural networks,” Neural Networks, vol. 6, no. 6, pp. 823–833, 1993.Google Scholar
  64. 64.
    M.D. Plumbley, “A Hebbian/anti-Hebbian network which optimizes information capacity by orthonormalizing the principal subspace,” in Proceedings of the IEE Artificial Neural Networks Conference, ANN-93, Brighton, UK, May 1993, pp. 86–90.Google Scholar
  65. 65.
    M.D. Plumbley and F. Fallside, “An information-theoretic approach to unsupervised connectionist models,” in Proceedings of the 1988 Connectionist Models Summer School, edited by D. Touretzky, G. Hinton, and T. Sejnowski, Morgan-Kaufmann, San Mateo, CA, 1988, pp. 239–245.Google Scholar
  66. 66.
    J. Rubner and P. Tavan, “A self-organizing network for principal component analysis,” Europhysics Letters, vol. 10, pp. 693–698, 1989.Google Scholar
  67. 67.
    D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Exploration in the Microstructure of Cognition, edited by D.E. Rumelhart and J.L. McClelland, MIT Press: Cambridge, MA, vol. 1, pp. 318–362, 1986.Google Scholar
  68. 68.
    D.E. Rumelhart and D. Zipser, “Competitive learning,” Cognitive Science, vol. 9, pp. 75–112, 1985.Google Scholar
  69. 69.
    T.D. Sanger, “Optimal unsupervised learning in a single-layer feedforward neural network,” Neural Networks, vol. 2, pp. 459–473, 1989.Google Scholar
  70. 70.
    E. Saund, “Dimensionality-reduction using connectionist networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 3, pp. 304–314, 1989.Google Scholar
  71. 71.
    J. Schmidhuber, “Learning factorial codes by predictability minimization,” Neural Computation, vol. 4, pp. 863–879, 1992.Google Scholar
  72. 72.
    J. Schmidhuber, “Learning unambiguous reduced sequence descriptions,” in Advances in Neural Information Processing Systems 4, Morgan Kaufmann, pp. 291–298, 1992.Google Scholar
  73. 73.
    N.N. Schraudolph and T.J. Sejnowski, “Competitive antihebbian learning of invariants,” in Advances in Neural Information Processing Systems 4, Morgan Kaufmann, pp. 1017–1024, 1992.Google Scholar
  74. 74.
    C.E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol. 27, pp. 379–423, 623–656, 1948.Google Scholar
  75. 75.
    A. Ukrainec and S. Haykin, “Application of unsupervised neural networks to the enhancement of polarization targets in dualpolarized radar images,” in IEEE Canadian Conference on Electrical and Computer Engineering, 1991.Google Scholar
  76. 76.
    C.von der Malsburg, “Self-organization of orientation sensitive cells in striate cortex,” Kybernetik, vol. 14, pp. 85–100, 1973.Google Scholar
  77. 77.
    S. Watanabe, Pattern Recognition: Human and Mechanical, John Wiley & Sons: New York, 1985.Google Scholar
  78. 78.
    A.S. Weigend, B.A. Huberman, and D.E. Rumelhart, “Predicting the future: A connectionist approach,” International Journal of Neural Systems, vol. 1, pp. 193–209, 1990.Google Scholar
  79. 79.
    R.J. Williams, “Feature discovery through error-correction learning,” Institute of Cognitive Science, University of California, San Diego, ICS Report 8501, 1985.Google Scholar
  80. 80.
    R. Zemel and G.E. Hinton, “Developing topographic representations by minimizing description length,” in Advances in Neural Information Processing System 6, edited by J.D. Cowan, G. Tesauro, and J. Alspector, Morgan Kaufmann, pp. 11–18, 1994.Google Scholar
  81. 81.
    R.S. Zemel and G.E. Hinton, “Discovering viewpoint-invariant relationships that characterize objects,” in Advances In Neural Information Processing Systems 3, edited by R.P. Lippmann, J.E. Moody, and D.S. Touretzky, Morgan Kaufmann Publishers, pp. 299–305, 1991.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Suzanna Becker
    • 1
  • Mark Plumbley
    • 2
  1. 1.Department of PsychologyMcMaster UniversityHamiltonCanada
  2. 2.Department of Computer ScienceKing's College, LondonLondonUK

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