Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Tony Jebara, Alex Pentland


We present the CEM (Conditional Expectation Maximi::ation) al(cid:173) gorithm as an extension of the EM (Expectation M aximi::ation) algorithm to conditional density estimation under missing data. A bounding and maximization process is given to specifically optimize conditional likelihood instead of the usual joint likelihood. We ap(cid:173) ply the method to conditioned mixture models and use bounding techniques to derive the model's update rules . Monotonic conver(cid:173) gence, computational efficiency and regression results superior to EM are demonstrated.