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Abstract:
We present the CEM (Conditional Expectation Maximization)
algorithm as an extension of the EM (Expectation Maximization)
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
apply the method to conditioned mixture models and use bounding
techniques to derive the model's update rules. Monotonic
convergence, computational efficiency and regression results
superior to EM are demonstrated.
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