A Supervised Clustering Algorithm for the Initialization of RBF Neural Network Classifiers

In this paper, we propose a new supervised clustering algorithm, coined as the homogeneous clustering (HC), to find the number and initial locations of the hidden units in radial basis function (RBF) neural network classifiers. In contrast to the traditional clustering algorithms introduced for this goal, the proposed algorithm is a supervised procedure where the number and initial locations of the hidden units are determined based on split of the clusters having overlaps among the classes. The basic idea of the proposed approach is to create class specific homogenous clusters where the corresponding samples are closer to their mean than the means of rival clusters belonging to other class categories. We tested the proposed clustering algorithm along with the RBF network classifier on the Graz02 object database and the ORL face database. The experimental results show that the RBF network classifier performs better when it is initialized with the proposed HC algorithm than an unsupervised k-means algorithm. Moreover, our recognition results exceed the best published results on the Graz02 database and they are comparable to the best results on the ORL face database indicating that the proposed clustering algorithm initializes the hidden unit parameters successfully.