Partial likelihood for real-time signal processing with finite normal mixtures

Author

Wang, Bo ; Adali, Tulay ; Liu, Xiao ; Xuan, Jianhua

Author_Institution

Dept. of Comput. Sci. & Electr. Eng., Maryland Univ., Baltimore, MD, USA

fYear

1998

fDate

31 Aug-2 Sep 1998

Firstpage

244

Lastpage

253

Abstract

We introduce a unified framework for nonlinear signal processing with finite normal mixtures (FNM) by using maximum partial likelihood (MPL) theory. We show that the equivalence of MPL to accumulated relative entropy minimization is valid for the FNM. Then, we define the information geometry of MPL and use the result to derive the EM algorithm for distribution learning based on the FNM model. The superior convergence of the EM algorithm as compared to the least relative entropy and the backpropagation algorithms is demonstrated by simulations. We also discuss the performance of the FNM based equalizers with different number of mixtures and observation vector sizes

Keywords

digital communication; entropy; maximum likelihood estimation; neural nets; real-time systems; signal processing; EM algorithm; accumulated relative entropy minimization; channel equalization; convergence; digital communication systems; expectation-maximisation algorithm; finite normal mixtures; information geometry; maximum partial likelihood theory; neural nets; nonlinear signal processing; real-time signal processing; Backpropagation algorithms; Computer science; Convergence; Entropy; Equalizers; Information geometry; Maximum likelihood estimation; Signal processing; Signal processing algorithms; Solid modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop

Conference_Location

Cambridge

ISSN

1089-3555

Print_ISBN

0-7803-5060-X

Type

conf

DOI

10.1109/NNSP.1998.710654

Filename

710654