DocumentCode :
3714619
Title :
A hybrid algorithm for non-negative matrix factorization based on symmetric information divergence
Author :
Karthik Devarajan;Nader Ebrahimi;Ehsan Soofi
Author_Institution :
Department of Biostatistics & Bioinformatics, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA 19111, United States
fYear :
2015
Firstpage :
1658
Lastpage :
1664
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.
Keywords :
Hybrid power systems
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BIBM.2015.7359924
Filename :
7359924
Link To Document :
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