Affiliation:
1. School of Sciences, Xi'an University of Technology, Xi'an 710054, China
2. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
Abstract
<p>In this paper, an active set recognition technique is suggested, and then a modified nonmonotonic line search rule is presented to enhance the efficiency of the nonmonotonic line search rule, in which we introduce a new parameter formula to attempt to control the nonmonotonic degree of the line search, and thus improve the chance of discovering the global minimum. By using a modified linear search and an active set recognition technique, a global convergence gradient solution for nonnegative matrix factorization (NMF) based on an alternating nonnegative least squares framework is proposed. We used a Barzilai-Borwein step size and greater step-size tactics to speed up the convergence. Finally, a large number of numerical experiments were carried out on synthetic and image datasets, and the results showed that our presented method was effective in calculating the speed and solution quality.</p>
Publisher
American Institute of Mathematical Sciences (AIMS)
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