Deterministic EM algorithms with penalties

Author

O´Sullivan, J.A. ; Snyder, Donald L.

Author_Institution

Dept. of Electr. Eng., Washington Univ., St. Louis, MO, USA

fYear

1995

fDate

17-22 Sep 1995

Firstpage

177

Abstract

Csiszar (1991) presented an axiomatic derivation of the use of the I-divergence as a discrepancy measure between nonnegative vectors. Snyder, Schulz, and O´Sullivan (see IEEE Trans. Signal Proc., vol.40, no.5, p.1143-50, 1992) then proposed the use of the I-divergence as an optimality criterion in deblurring problems, and introduced the deterministic version of the EM algorithm. Byrne (see IEEE Trans. Image Proc., vol.2, pp.96-103, Jan. 1993) used a similar scenario to Snyder et. al., but also looked at reverse entropy measures and included maximum entropy penalties. O´Sullivan introduced roughness penalties for use in stochastic problems where the use of Markov random fields may not arise naturally; these penalties are used inn this article for the deterministic problem

Keywords

algorithm theory; deterministic algorithms; maximum entropy methods; optimisation; parameter estimation; stochastic processes; I-divergence; axiomatic derivation; deblurring problems; deterministic EM algorithms; deterministic problem; discrepancy measure; maximum entropy penalties; nonnegative vectors; optimality criterion; parameter estimation; reverse entropy measures; roughness penalties; stochastic problems; Aging; Assembly; Convergence; Electric variables measurement; Entropy; Markov random fields; Matrix decomposition; Minimization methods; Parameter estimation; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1995. Proceedings., 1995 IEEE International Symposium on

Conference_Location

Whistler, BC

Print_ISBN

0-7803-2453-6

Type

conf

DOI

10.1109/ISIT.1995.531526

Filename

531526