Title :
The α-EM algorithm and its applications
Author :
Matsuyama, Yasuo
Author_Institution :
Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan
Abstract :
The α-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 α-logarithm. The case of α=-1 corresponds to the logarithm. The number α 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 α-FM algorithm and examples are shown
Keywords :
Hessian matrices; Jacobian matrices; convergence of numerical methods; eigenvalues and eigenfunctions; iterative methods; maximum likelihood estimation; optimisation; parameter estimation; α-EM algorithm; α-logarithm; convergence speed; eigenvalues; estimation parameter; expectation-maximization algorithm; extended logarithm; incomplete data likelihood ratio; iterative optimization; optimization function; update matrices; Convergence; Cost function;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
0-7803-6293-4
DOI :
10.1109/ICASSP.2000.862051
