PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Convex relaxation of mixture regression with efficient algorithms
Novi Quadrianto, Tiberio Caetano, John Lim and Dale Schuurmans
In: 23rd Annual Conference on Neural Information Processing Systems, 7-12 Dec 2009, Vancouver, B.C., Canada.


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 updates. We evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5556
Deposited By:Novi Quadrianto
Deposited On:25 February 2010