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publ. Springer-Verlag, London, pp. 555-560 (1998-09-02) (bibtex)

## Learning invariance manifolds.

Laurenz Wiskott

*Abstract:* A new algorithm for learning invariance manifolds is introduced
that allows a neuron to learn a non-linear transfer function to extract
invariant or rather slowly varying features from a vectorial input
sequence. This is generalized to a group of neurons, referred to as a
Gibson-clique, to learn slowly varying features that are uncorrelated.
Since the transfer functions are non-linear, this technique can be applied
iteratively to learn more and more complex and invariant features in a
hierarchical architecture. Two simple examples demonstrate the general
properties of the learning algorithm.

### Relevant Project:

May 25, 1998, Laurenz Wiskott, http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/wiskott/