Title :
A likelihood framework for nonlinear signal processing with finite normal mixtures
Author :
Adali, Tulay ; Bo Wang ; Xiao Liu ; Jianhua Xuan
Author_Institution :
Dept. of Comput. Sci. & Electr. Eng., Univ. of Maryland Baltimore County, Baltimore, MD, USA
Abstract :
We introduce a likelihood framework for nonlinear signal processing using partial likelihood and use the result to derive the information geometric em algorithm for distribution learning through information-theoretic projections. We demonstrate the superior convergence of the em algorithm as compared to least relative entropy (LRE) algorithm by simulations. The performance of finite normal mixtures (FNM) based equalizers with different number of mixtures and different dimension observation vectors is also discussed.
Keywords :
maximum likelihood estimation; signal processing; dimension observation vectors; distribution learning; finite normal mixtures; information-theoretic projections; least relative entropy algorithm; likelihood framework; nonlinear signal processing; partial likelihood; Bit error rate; Equalizers; Minimization; Neural networks; Signal processing algorithms; Signal to noise ratio;
Conference_Titel :
Signal Processing Conference (EUSIPCO 1998), 9th European
Conference_Location :
Rhodes
Print_ISBN :
978-960-7620-06-4
