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Friday, May 18
Computational Statistics
Bayesian Computations and Applications
Fri, May 18, 10:30 AM - 12:00 PM
Grand Ballroom E

Non-Negative Matrix Factorization for The Exponential Family Based on Generalized Dual Divergence and Intrinsic Information (304353)

*Karthik Devarajan, Fox Chase Cancer Center, Temple University Health System 
Nader Ebrahimi, Northern Illinois University 
Ehsanolah Soofi, University of Wisconsin at Milwaukee 

Keywords: nonnegative matrix factorization, Kullback-Leibler divergence, dual divergence, intrinsic information, symmetric information divergence, exponential family, EM algorithm, high-dimensional data

A unified approach to non-negative matrix factorization based on generalized dual Kullback-Leibler divergence and intrinsic information, which embeds the exponential family of models within a theoretical framework, is proposed. A family of algorithms is developed using this framework including rigorous proofs of convergence. This approach generalizes existing methods and contrasts with the recently proposed quasi-likelihood approach, thus providing a flexible alternative for non-negative matrix factorization.