Maximum and Minimum Likelihood Hebbian learning for exploratory projection pursuit

Data Mining and Knowledge Discovery

Volumn 8, Issue 3, 2004, Pages 203-225

  Corchado, Emilio ^a   MacDonald, Donald ^a   Fyfe, Colin ^a  

^a UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

Artificial neural networks; Exploratory projection pursuit

Indexed keywords

DATA COMPRESSION; DATA MINING; DATA REDUCTION; FEEDBACK; INFORMATION ANALYSIS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DENSITY FUNCTION;

DATA MINING COMMUNITY; DATA SETS; EXPLORATORY PROJECTION PURSUIT (EPP); GAUSSIAN DISTRIBUTIONS;

NEURAL NETWORKS;

EID: 3543075142     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:DAMI.0000023673.23078.a3     Document Type: Review

References (20)

1. Bell, J.F., Owensby, P.D., Hawke, B.R., and Gaffey, M.J. 1988. The 52 colour asteroid survey: Final results and interpretation. Lunar Planet Sci. Conf., XIX:57 (abstract).
2. Bishop, C.M. 1995. Neural Networks for Pattern Recognition. Oxford.
3. Diaconis, P. and Freedman, D. 1984. Asymptotics of graphical projections. The Annals of Statistics, 12(3):793-815.
4. Fyfe, C. 1993. PCA properties of interneurons. From Neurobiology to Real World Computing, Proceedings of International Conference on Artificial on Artificial Neural Networks, ICAAN 93, pp. 183-188.
5. Fyfe, C. and Baddeley, R. 1995. Non-linear data structure extraction using simple Hebbian networks. Biological Cybernetics, 72(6):533-541.
6. Fyfe, C. 1995. Negative feedback as an organising principle for artificial neural networks. Ph.D. Thesis, Strathclyde University.
7. Fyfe, C. and MacDonald, D. 2002. ε-insensitive Hebbian learning. Neurocomputing (Accepted for publication).
8. Howell, E.S., Merenyi, E., and Lebofsky, L.A., 1994. Using neural networks to classify asteroid spectra. Journal of Geophysical Research, 99(E5):10847-10865.
9. Hyvärinen, A. 2001. Complexity pursuit: Separating interesting components from time series. Neural Computation, 13:883-898.
10. Hyvärinen, A., Karhunen, J., and Oja, E. 2002. Independent Component Analysis. Wiley, ISBN 0-471-40540-X.
11. Karhunen, J. and Joutsensalo, J. 1994. Representation and separation of signals using non-linear PCA type learning. Neural Networks, 7:113-127.
12. Kashyap, R.L., Blaydon, C.C., and Fu, K.S. 1994. Stochastic approximation. In a Prelude to Neural Networks: Adaptive and Learning Systems, M. Jerry (Ed.), Mendel, Prentice Hall, ISBN 0-13-147448-0.
13. Lai, P.L., Charles, D., and Fyfe, C. 2000. Seeking independence using biologically inspired artificial neural networks. In Developments in Artificial Neural Network Theory: Independent Component Analysis and Blind Source Separation, M.A. Girolami (Ed.), Springer Verlag.
14. MacDonald, D., McGlinchey, S., Kawala, J., and Fyfe, C. 1999. Comparison of Kohonen, scale-invariant and GTM self-organising maps for interpretation of spectral data. European Symposium on Artificial Neural Networks (ESANN'99), 117-122.
15. Oja, E. 1989. Neural networks, principal components and subspaces. International Journal of Neural Systems, 1:61-68.
16. Oja, E., Ogawa, H., and Wangviwattana, J. 1992. Principal components analysis by homogeneous neural networks, Part 1. The Weighted Subspace Criterion, IEICE Transaction on Information and Systems, E75D:366-375.
17. Smola, A. J. and Scholkopf, B. 1998. A tutorial on support vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series.
18. Tholen, D. 1994. Asteroid taxonomy from cluster analysis of photometry. Ph.D. dissertation, University of Arizona.
19. Xu, L. 1993. Least mean square error reconstruction for self-organizing nets. Neural Networks, 6:627-648.
20. Zellner, B., Tholen, D.J., and Tedesco, E.F. 1985. The eight-colour asteroid survey: Results fro 589 minor planets. Icarus, 355-416.