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

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