Hidden node pruning of multilayer perceptrons based on redundancy reduction

Authors:
Sang-Hoon Oh
Affiliations:
Mokwon University, Daejon, Korea
Venue:
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Year:
2011

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Abstract

Among many approaches to choosing the proper size of neural networks, one popular approach is to start with an oversized network and then prune it to a smaller size so as to attain better performance with less computational complexity. In this paper, a new hidden node pruning method is proposed based on the redundancy reduction among hidden nodes. The redundancy information is given by correlation coefficients among hidden nodes and this can save computational complexity. Experimental results demonstrate the effectiveness of the proposed method.