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33

NIPS
2004

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Maximum-Margin Matrix Factorization

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Maximum-Margin Matrix Factorization

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We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margin linear discrimination. We show how to learn low-norm factorizations by solving a semi-definite program, and discuss generalization error bounds for them.

Nathan Srebro, Jason D. M. Rennie, Tommi Jaakkola

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Generalization Error Bounds | Large-margin Linear Discrimination | Low-rank Factorizations | NIPS 2004 | NIPS 2007 |

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Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2004
Where	NIPS
Authors	Nathan Srebro, Jason D. M. Rennie, Tommi Jaakkola

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