Neural Computation
August 15, 1998, Vol. 10, No. 6, Pages 1445-1454
(doi: 10.1162/089976698300017250)
A Sparse Representation for Function Approximation
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Abstract
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.