Dimension lower bounds for linear approaches to function approximation
Abstract
This short note presents a linear algebraic approach to proving dimension lower bounds for linear methods that solve L2 function approximation problems. The basic argument has appeared in the literature before (e.g., Barron, 1993) for establishing lower bounds on Kolmogorov n-widths. The argument is applied to give sample size lower bounds for kernel methods.
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