Maximum Conditional Likelihood via Bound Maximization and the CEM Algorithm

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.

Authors

• Tony Jebara
• Alex Pentland

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