Efficient and accurate ℓ p-norm multiple kernel learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

188 Citations (Scopus)

Abstract

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 2009 Conference
Pages997-1005
Number of pages9
Publication statusPublished - 2009
Event23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Vancouver, BC, Canada
Duration: 2009 Dec 72009 Dec 10

Publication series

NameAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Other

Other23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
CountryCanada
CityVancouver, BC
Period09/12/709/12/10

ASJC Scopus subject areas

  • Information Systems

Cite this

Kloft, M., Brefeld, U., Sonnenburg, S., Laskov, P., Müller, K. R., & Zien, A. (2009). Efficient and accurate ℓ p-norm multiple kernel learning. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 997-1005). (Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference).