Parallelizing Spectral Algorithms for Kernel Learning
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We consider a distributed learning approach in supervised learning for a large class of spectral regularization methods in an RKHS framework. The data set of size n is partitioned into m=O(n^α) disjoint subsets. On each subset, some spectral regularization method (belonging to a large class, including in particular Kernel Ridge Regression, L^2-boosting and spectral cut-off) is applied. The regression function f is then estimated via simple averaging, leading to a substantial reduction in computation time. We show that minimax optimal rates of convergence are preserved if m grows sufficiently slowly (corresponding to an upper bound for α) as n →∞, depending on the smoothness assumptions on f and the intrinsic dimensionality. In spirit, our approach is classical.
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