A matrix modular neural network based on task decomposition with subspace division by adaptive affinity propagation clustering

Abstract

In this paper, a matrix modular neural network (MMNN) based on task decomposition with subspace division by adaptive affinity propagation clustering is developed to solve classification tasks. First, we propose an adaptive version to affinity propagation clustering, which is adopted to divide each class subspace into several clusters. By these divisions of class spaces, a classification problem can be decomposed into many binary classification subtasks between cluster pairs, which are much easier than the classification task in the original multi-class space. Each of these binary classification subtasks is solved by a neural network designed by a dynamic process. Then all designed network modules form a network matrix structure, which produces a matrix of outputs that will be fed to an integration machine so that a classification decision can be made. Finally, the experimental results show that our proposed MMNN system has more powerful generalization capability than the classifiers of single 3-layered perceptron and modular neural networks adopting other task decomposition techniques, and has a less training time consumption.