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This paper proposes an efficient framework for learning a high-dimensional nonlinear mapping using compressed sensing techniques. Given a training data set of the input and output pairs of the mapping to be learned, our framework reduces both the dimensionalities of the input and output spaces by efficient computation of random projection, and then learns a nonlinear mapping between the low-dimensional...
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