Efficient and accurate ℓp-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

    215 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, ℓ1-norm MKL is hardly observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures, we generalize MKL to arbitrary ℓp-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
    PublisherNeural Information Processing Systems
    Pages997-1005
    Number of pages9
    ISBN (Print)9781615679119
    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
    Country/TerritoryCanada
    CityVancouver, BC
    Period09/12/709/12/10

    ASJC Scopus subject areas

    • Information Systems

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