Non-linear data structure extraction using simple hebbian networks |
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Authors: | Colin Fyfe Roland Baddeley |
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Institution: | (1) Department of Computer Science, University of Strathclyde, UK, GB;(2) Department of Experimental Psychology, University of Oxford, South Parks Rd, Oxford OX1 3UD, UK, GB |
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Abstract: | . We present a class a neural networks algorithms based on simple hebbian learning which allow the finding of higher order
structure in data. The neural networks use negative feedback of activation to self-organise; such networks have previously
been shown to be capable of performing principal component analysis (PCA). In this paper, this is extended to exploratory
projection pursuit (EPP), which is a statistical method for investigating structure in high-dimensional data sets. As opposed
to previous proposals for networks which learn using hebbian learning, no explicit weight normalisation, decay or weight clipping
is required. The results are extended to multiple units and related to both the statistical literature on EPP and the neural
network literature on non-linear PCA.
Received: 30 May 1994/Accepted in revised form: 18 November 1994 |
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Keywords: | |
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