Optimizing locality of data representation in MLP networks

Local learning techniques associated with multilayer perceptron (MLP) networks typically rely on integrating receptive fields into the network model. However, data representation schemes that employ multiple, overlapping receptive fields to preprocess network inputs can be another source of local learning in MLP networks. This paper demonstrates a preprocessing scheme in which a genetic algorithm is used to determine the form and placement of receptive fields in order to optimize locality of representation of MLP network inputs. A performance metric for comparing MLP networks with disparate degrees of freedom is introduced. Both the preprocessing scheme and the proposed metric are demonstrated by using them in the context of predicting the Mackey-Glass chaotic time series