Topological separation versus weight sharing in neural net optimization

Abstract

Recent advances in neural networks application development for real life problems have drawn attention to network optimization. Most of the known optimization methods rely heavily on a weight sharing concept for pattern separation and recognition. The shortcoming of the weight sharing method is attributed to a large number of extraneous weights which play a minimal role in pattern separation and recognition. Our experiments have shown that up to 97% of the connections in the network can be eliminated with little or no change in the network performance.Topological separation should be used when the size of the network is large enough to tackle real life problems such as fingerprint classification. Our research has focused on the network topology by changing the number of connections as secondary method of optimization. Our findings indicate that for large networks, topological separation yields smaller network size that is more suitable for VLSI implementation. Topological separation is based on the error surface and information content of the network. As such it is an economical way of size reduction which leads to overall optimization. The differential pruning of the connections is based on the weight contents rather than number of connections. The training error may vary with the topological dynamics but the correlation between the error surface and recognition rate decreases to a minimum. Topological separation reduces the size of the network by changing its architecture without degrading its performance.

Evaluation of pattern classifiers for fingerprint and OCR applications

“…A standard method of error minimization for real world problems is backpropagation 27] although more powerful methods of optimization have also been used 28,29]. In addition to the problem of error reduction, e ective generalization also requires that the information content of the network be reduced to some minimum value 30,31,32]. The resulting reduced network has the advantage of increased speed achieved by using fewer connections and is more e ective in terms of the use of information capacity to achieve a speci ed pattern recognition accuracy.…”

“…This results in a smaller network with a very high information content that allows the use of a reasonably small training set. We have used the Boltzmann method as a secondary method of optimization to prune the networks used here 30,31]. The method can be used in conjunction with a primary method of optimization such as a scaled conjugate gradient scheme 29].…”