The paper derives sharp matching convergence rates for spectral methods in linear regression via feature space decomposition, enabling pre-ordering of algorithms and generalizing saturation effects.
[EHN96] Heinz Werner Engl, Martin Hanke, and Andreas Neubauer.Regularization of Inverse Problems, volume 375 ofMathematics and Its Applications
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Sharp convergence rates for Spectral methods via the feature space decomposition method
The paper derives sharp matching convergence rates for spectral methods in linear regression via feature space decomposition, enabling pre-ordering of algorithms and generalizing saturation effects.