A New Neural Implementation of Exploratory Projection Pursuit
We investigate an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function(pdf). We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors  have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit(EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.
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- 1.Bishop, C.M, Neural Networks for Pattern Recognition, Oxford, 1995.Google Scholar
- 3.Fyfe, C., “PCA Properties of Interneurons”, From Neurobiology to Real World Computing, Proceedings of International Conference on Artificial on Artificial Neural Networks, ICAAN93, pages 183–188, 1993.Google Scholar
- 5.Fyfe, C. and MacDonald, D., •-Insensitive Hebbian learning, Neurocomputing, 2001Google Scholar
- 9.Oja, E., Ogawa, H., Wangviwattana, J., Principal Components Analysis by Homogeneous Neural Networks, part 1, The Weighted Subspace Criterion, IEICE Transaction on Information and Systems, E75D: 366–375, May 1992.Google Scholar
- 10.Oimola, A.J. and. Scholkopf, B. A Tutorial on Support Vector Regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series, Oct.1998.Google Scholar