Abstract:
We propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. The proposed method, Maximum Likelihood Mu...Show MoreMetadata
Abstract:
We propose a new method of approximating mutual information based on maximum likelihood estimation of a density ratio function. The proposed method, Maximum Likelihood Mutual Information (MLMI), possesses useful properties, e.g., it does not involve density estimation, the global optimal solution can be efficiently computed, it has suitable convergence properties, and model selection criteria are available. Numerical experiments show that MLMI compares favorably with existing methods.
Published in: 2009 IEEE International Symposium on Information Theory
Date of Conference: 28 June 2009 - 03 July 2009
Date Added to IEEE Xplore: 18 August 2009
ISBN Information: