The /spl alpha/-EM algorithm and its applications

Authors:
Y. Matsuyama
Affiliations:
Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan
Venue:
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01
Year:
2000

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Abstract

The /spl alpha/-EM algorithm is a super-class of the traditional expectation-maximization (EM) algorithm. This algorithm is derived by computing the likelihood ratio of incomplete data through an extended logarithm; namely, the /spl alpha/-logarithm. The case of /spl alpha/=-1 corresponds to the logarithm. The number /spl alpha/ adjusts eigenvalues of update matrices by reflecting the optimization function's second-order properties with respect to the estimation parameter. This property shows merits on speedup of convergence. In the paper, a derivation of the algorithm is given first. Then, convergence and speedup properties are discussed. Finally, the applicability of the /spl alpha/-FM algorithm and examples are shown.