Quadrianto, N and Caetano, TS and Lim, J and Schuurmans, D (2009) Convex relaxation of mixture regression with efficient algorithms. Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. pp. 1491-1499.Full text not available from this repository.
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.
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 12:20|
|Last Modified:||08 Dec 2014 02:27|