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Pattern recognition using information slicing method (PRISM)

In this paper we present a method of partitioning feature space of given data into a number of hypercuboids. We derive the overall complexity of the classification problem as a weighted sum of the hypercube's separability measure and the number of elements present in them. On a total of eight Gaussian distributions and two UCI pattern recognition benchmarks, we quantify the complexity of the classification problem. Also, we discuss how our approach can be used to solve a range of pattern recognition problems in a non-conventional but highly effective manner.