Sparsity-accuracy trade-off in MKL

Abstract

We empirically investigate the best trade-off between sparse and uniformly-weighted multiple kernel learning (MKL) using the elastic-net regularization on real and simulated datasets. We find that the best trade-off parameter depends not only on the sparsity of the true kernel-weight spectrum but also on the linear dependence among kernels and the number of samples.

8pages, 2 figures

References in corpus (2)

• Consistency of the group Lasso and multiple kernel learning
• L2 Regularization for Learning Kernels

Cited by in corpus (3)

• Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness
• Regularization Strategies and Empirical Bayesian Learning for MKL
• Sharp Convergence Rate and Support Consistency of Multiple Kernel Learning with Sparse and Dense Regularization