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The α-EM algorithm: surrogate likelihood maximization using α-logarithmic information measures

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1 Author(s)
Matsuyama, Y. ; Dept. of Comput. Sci., Waseda Univ., Tokyo, Japan

A new likelihood maximization algorithm called the α-EM algorithm (α-expectation-maximization algorithm) is presented. This algorithm outperforms the traditional or logarithmic EM algorithm in terms of convergence speed for an appropriate range of the design parameter α. The log-EM algorithm is a special case corresponding to α=-1. The main idea behind the α-EM algorithm is to search for an effective surrogate function or a minorizer for the maximization of the observed data's likelihood ratio. The surrogate function adopted in this paper is based upon the α-logarithm which is related to the convex divergence. The convergence speed of the α-EM algorithm is theoretically analyzed through α-dependent update matrices and illustrated by numerical simulations. Finally, general guidelines for using the α-logarithmic methods are given. The choice of alternative surrogate functions is also discussed.

Published in:

Information Theory, IEEE Transactions on  (Volume:49 ,  Issue: 3 )