Three heuristics for receptive field optimization for ensemble encoding

Ensemble encoding is a biologically-motivated, distributed data representation scheme for MLP networks. Multiple overlapping receptive fields are used to enhance locality of representation. The number, form, and placement of receptive fields has a great impact on performance. We present three heuristics, two based on descriptive statistics, and one based on clustering, for optimizing receptive field configuration, and compare their performance on three benchmark data sets. Performance varies among the benchmarks, but on one benchmark, the clustering heuristic yields a 56% improvement in test set classification over unencoded data, and a 48% improvement over symmetrical-placement three-receptor ensemble encoding.