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Informative representation learning for automatic target recognition

Informative representations are those representations that do more than reconstruct the data; they have information embedded implicitly in them and are compressive for utilization in real-time Automatic Target Recognition. In this paper we create methods for embedding information in subspace bases through sparsity and information theoretic measures. We present a theory of informative bases and demonstrate some practical examples of basis learning using infrared imagery. We will employ sparsity and entropy measures to drive the learning process to extract the most informative representation and will draw relations between informative representations and the quadratic correlation filter.