A Sparse Representation for Function Approximation.
We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to principal component analysis, regularization, sparsity principles, and support vector machines.
Massachusetts Institute of Technology, Artificial Intelligence Lab, Cambridge MA, US, 545 Technology Square, 02139. tp@ai.mit.edu
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