Graph theoretic techniques for pruning data and their applications

In pattern recognition tasks, we usually do not pay much attention to the arbitrarily chosen training set of a pattern classiier beforehand. This paper proposes several methods for pruning data sets based on graph theory in order to alleviate the redundancy in the original data set whilst retaining the original data structure as far as possible. The proposed methods are applied to the training sets for pattern recognition by a Multi-Layered Perceptron Neural Network(MLP-NN) and the locations of centroids of a Radial Basis Function Neural Network (RBF-NN). The advantage of the proposed graph theoretic methods is that they do not require any calculation for statistical distributions of the respective clusters. The experimental results in comparison with both k-means clustering and Learning Vector Quantisation(LVQ) methods show that the proposed methods give encouraging performances in terms of computation for data classiication tasks.