Abstract
We present a neural network algorithm based on simple Hebbian learning which allows the finding of higher order structure in data. The neural network uses 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.
Recently, it has been proposed [3, 5] that one way of choosing an appropriate filter for processing a particular domain is to find the filter with the highest output kurtosis. We pursue this avenue further by using the developed neural network to find the filter with the highest output kurtosis when applied to a collection of natural images. The method does not appear to work but interesting lessons can be derived from our failure.
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© 1995 Springer-Verlag London
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Fyfe, C., Baddeley, R. (1995). Edge Enhancement and Exploratory Projection Pursuit. In: Smith, L.S., Hancock, P.J.B. (eds) Neural Computation and Psychology. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3579-1_8
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DOI: https://doi.org/10.1007/978-1-4471-3579-1_8
Publisher Name: Springer, London
Print ISBN: 978-3-540-19948-9
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