Efficient and accurate ℓ p-norm multiple kernel learning

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Authors

  • Marius Kloft
  • Ulf Brefeld
  • Soren Sonnenburg
  • Pavel Laskov
  • Klaus Robert Müller
  • Alexander Zien

Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability. Unfortunately, ℓ 1-norm MKL is hardly observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures, we generalize MKL to arbitrary ℓ p-norms. We devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for arbitrary p > 1. Empirically, we demonstrate that the interleaved optimization strategies are much faster compared to the traditionally used wrapper approaches. Finally, we apply ℓp-norm MKL to real-world problems from computational biology, showing that non-sparse MKL achieves accuracies that go beyond the state-of-the-art.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 22 : Proceedings of the 23rd Annual Conference on Neural Information Processing Systems 2009
EditorsYoshua Bengio, Dale Schuurmans, John Lafferty, Chris Williams, Aron Culotta
Number of pages9
PublisherNeural Information Processing Systems
Publication date2009
Pages997-1005
ISBN (print)978-161567911-9
Publication statusPublished - 2009
Externally publishedYes
Event23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Hyatt Regency Vancouver, Vancouver, BC, Canada
Duration: 07.12.200910.12.2009
Conference number: 23
https://nips.cc/Conferences/2009