Circular Backpropagation Networks Embed Vector Quantization

Sandro Ridella, Stefano Rovetta, and Rodolfo Zunino


Abstract

This letter proves the equivalence between vector quantization
(VQ) classifiers and circular backpropagation (CBP) networks.
The calibrated prototypes for a VQ schema can be plugged in
a CBP feedforward structure having the same number of hidden
neurons and featuring the same mapping. The letter describes
how to exploit such equivalence by using VQ prototypes to
perform a meaningful initialization for BP optimization. The
approach effectiveness was tested considering a real
classification problem (NIST handwritten digits).


Index Terms

Feedforward neural networks, optical character recognition,
vector quantization.



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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 4,
JULY 1999, pp. 972-975