A Dimensionality Reduction Approach for Modular Neural Networks

Abstract

A modular neural network architecture is composed by independent neural networks that focus on different parts of the whole task. This work proposes the Intrinsic Modular Neural Networks that aims not only to reduce the number of classes and patterns in each independent neural network, but also to reduce the dimensionality of the data. The task decomposition is performed by the High-Dimensional Data Clustering algorithm. After the clustering, the training patterns are divided in groups and each group is used to train an independent neural network. Experiments on public databases show promising results.