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Random Projections for Spectral Algorithms: Optimal Rates in Sobolev Norms
Random projections (RP) offer an effective method for reducing computational and storage overhead while preserving the geometric structure of data. However, existing studies have primarily focused on the optimal generalization performance of specific kernel-regularized algorithms with RP in the well
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