Title :
Convergence tradeoffs for EM-type algorithms
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Abstract :
The author analyzes the convergence properties of the EM algorithm for iteratively approximating the maximum likelihood estimate. A radius of convergence is specified and the asymptotic rate of convergence of the algorithm derived via the multivariate Taylor expansion with remainder. The radius and rate of convergence generally depend on the choice of complete data. The results can be used to evaluate different choices of complete data space in terms of algorithm performance
Keywords :
convergence of numerical methods; iterative methods; maximum likelihood estimation; signal processing; algorithm performance; convergence; expectation maximisation algorithm; maximum likelihood estimate; multivariate Taylor expansion; Convergence; Eigenvalues and eigenfunctions; Iterative algorithms; Maximum likelihood estimation; Signal processing algorithms; Spectral analysis; Symmetric matrices;
Conference_Titel :
Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-0508-6
DOI :
10.1109/SSAP.1992.246851
