Simplified neuron model as a principal component analyzer |
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| Authors: | Erkki Oja |
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| Affiliation: | (1) Institute of Mathematics, University of Kuopio, 70100 Kuopio 10, Finland |
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| Abstract: | A simple linear neuron model with constrained Hebbian-type synaptic modification is analyzed and a new class of unconstrained learning rules is derived. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. |
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| Keywords: | Neuron models Synaptic plasticity Stochastic approximation |
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