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A Hybrid Algorithm for Non-negative Matrix Factorization Based on Symmetric Information Divergence
Karthik Devarajan1, Nader Ebrahimi2, Ehsan Soofi3
1Department of Biostatistics & Bioinformatics, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA 19111.
Abstract:
The objective of this paper is to provide a hybrid algorithm for non-negative matrix factorization based on a symmetric version of Kullback-Leibler divergence, known as intrinsic information. The convergence of the proposed algorithm is shown for several members of the exponential family such as the Gaussian, Poisson, gamma and inverse Gaussian models. The speed of this algorithm is examined and its usefulness is illustrated through some applied problems.
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