A Generalized Divergence Measure for Nonnegative Matrix Factorization

Authors:
Raul Kompass
Affiliations:
FU Berlin, Institut für Mathematik und Informatik, 14152 Berlin, Germany, kompass@inf.fu-berlin.de
Venue:
Neural Computation
Year:
2007

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Abstract

This letter presents a general parametric divergence measure. The metric includes as special cases quadratic error and Kullback-Leibler divergence. A parametric generalization of the two different multiplicative update rules for nonnegative matrix factorization by Lee and Seung (2001) is shown to lead to locally optimal solutions of the nonnegative matrix factorization problem with this new cost function. Numeric simulations demonstrate that the new update rule may improve the quadratic distance convergence speed. A proof of convergence is given that, as in Lee and Seung, uses an auxiliary function known from the expectation-maximization theoretical framework.