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Convex relaxation of mixture regression with efficient algorithms

Quadrianto, Novi; Caetano, Tiberio; Lim, John; Schuurmans, Dale


We develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable, we reformulate the problem to eliminate the need for general semidefinite programming. In particular, we provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form...[Show more]

CollectionsANU Research Publications
Date published: 2009
Type: Conference paper
Source: Proceedings of The 23rd Annual Conference on Neural Information Processing Systems (NIPS 23)


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