Information geometry of maximum partial likelihood estimation for channel equalization

Information geometry of partial likelihood is constructed and is used to derive the em-algorithm for learning parameters of a conditional distribution model through information-theoretic projections. To construct the coordinates of the information geometry, an expectation maximization (EM) framework is described for the distribution learning problem using the Gaussian mixture probability model. It is shown that the information-geometric em-algorithm is equivalent to EM to establish its convergence. The algorithm is applied to channel equalization by distribution learning and its rapid convergence characteristics are demonstrated through simulation studies