Efficient and accurate ℓ p-norm multiple kernel learning
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
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 language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 22 : Proceedings of the 23rd Annual Conference on Neural Information Processing Systems 2009 |
| Editors | Yoshua Bengio, Dale Schuurmans, John Lafferty, Chris Williams, Aron Culotta |
| Number of pages | 9 |
| Publisher | Neural Information Processing Systems |
| Publication date | 2009 |
| Pages | 997-1005 |
| ISBN (print) | 978-161567911-9 |
| Publication status | Published - 2009 |
| Externally published | Yes |
| Event | 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Hyatt Regency Vancouver, Vancouver, BC, Canada Duration: 07.12.2009 → 10.12.2009 Conference number: 23 https://nips.cc/Conferences/2009 |
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