Sparsity in Multiple Kernel Learning

  title={Sparsity in Multiple Kernel Learning},
  author={Vladimir Koltchinskii and Ming Yuan},
The problem of multiple kernel learning based on penalized empirical risk minimization is discussed. The complexity penalty is determined jointly by the empirical L2 norms and the reproducing kernel Hilbert space (RKHS) norms induced by the kernels with a data-driven choice of regularization parameters. The main focus is on the case when the total number of kernels is large, but only a relatively small number of them is needed to represent the target function, so that the problem is sparse. The… CONTINUE READING
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Sparse recovery in convex hulls via entropy penalization

  • Ann. Statist. 37 1332–1359. MR2509076
  • 2009
2 Excerpts

Consistency of the group lasso and multiple kernel learning

  • F. BACH
  • J. Mach. Learn. Res. 9 1179–1225. MR2417268
  • 2008
3 Excerpts

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