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Title: An unsupervised learning rule for vector normalization and gain control
Authors: Van Hulle, Marc #
Issue Date: 1993
Host Document: Proceedings pages:1202-1206
Conference: IEEE International Conference on Neural Networks location:San Francisco, USA date:28 Mar -01 Apr 1993
Abstract: A neural network model is proposed for linear processing units and an unsupervised learning rule for normalizing input vectors drawn from a given probability distribution. After training the network, the gain with which the inputs are sampled is set at such a level that it yields a fixed mapping between the root of the average squared norm of the input vectors and the norm of the processing unit's outputs. Three cases of normalization are considered. The adaptive network simultaneously performs error correction and recalibration. It is shown that the author's learning rule solves the dual problem of Oja's single-unit unsupervised learning rule
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Research Group Neurophysiology
# (joint) last author

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