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A q -Parameterized Deterministic Annealing EM Algorithm Based on Nonextensive Statistical Mechanics

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2 Author(s)
Wenbin Guo ; Sch. of Telecommun. Eng., Beijing Univ. of Posts & Telecommun., Beijing ; Shuguang Cui

We propose a q-parameterized deterministic annealing expectation maximization (q-DAEM) algorithm for parameter estimation motivated by the concept of Tsallis entropy that originates from the nonextensive statistical mechanics. The q-DAEM algorithm combines the feature of annealing algorithms to reduce initialization sensitivity and that of q-EM algorithms to achieve fast convergence. The q-EM algorithm is a one-parameter generalized EM algorithm that has been previously proposed by the authors. By interpreting the EM algorithm via likelihood lower bound maximization, we build the fundamental interconnections among DAEM, q-DAEM, and statistical mechanics. To illustrate the benefits of using q-DAEM, we investigate two applications: finite mixture model estimation in data clustering; and joint channel estimation and data detection in communication systems, where we show that the q-DAEM algorithm achieves superior performance over the EM algorithm for both applications.

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Signal Processing, IEEE Transactions on  (Volume:56 ,  Issue: 7 )