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Learning from an ensemble of Receptive Fields

In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as Shape Encoding Receptive Fields (SERF), is able to perform fast and accurate data classification and regression of multi-dimensional data. A SERF is a histogram structure that encodes the shape of multi-dimensional data relative to its center, in a manner similar to the neural coding of sensory stimulus by the receptive fields. The bins of this histogram represent a local region in an n-dimensional space. During the training phase, an ensemble of K SERF structures are initialized and data is summarized into the corresponding bins of each SERF structure. The collection of local data summaries makes each SERF a coarse nonlinear data predictor over the entire feature space. The output prediction of an unknown query is computed by the weighted aggregation of the hypotheses of the ensemble of K SERFs. In our series of experiments, we demonstrate the model's superiority to perform fast and accurate data prediction.