Inexact block coordinate descent methods with application to non-negative matrix factorization

2011 ◽  
Vol 31 (4) ◽  
pp. 1431-1452 ◽  
Author(s):  
S. Bonettini
Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 540
Author(s):  
Soodabeh Asadi ◽  
Janez Povh

This article uses the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalize the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain time one matrix factor is fixed and the other is updated by moving along the steepest descent direction computed from the penalized objective function and projecting onto the space of non-negative matrices. Our method is tested on two sets of synthetic data for various values of penalty parameters. The performance is compared to the well-known multiplicative update (MU) method from Ding (2006), and with a modified global convergent variant of the MU algorithm recently proposed by Mirzal (2014). We provide extensive numerical results coupled with appropriate visualizations, which demonstrate that our method is very competitive and usually outperforms the other two methods.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Fanhua Shang ◽  
Zhihui Zhang ◽  
Yuanyuan Liu ◽  
Hongying Liua ◽  
Jing Xu

2016 ◽  
Vol 163 (1-2) ◽  
pp. 85-114 ◽  
Author(s):  
Mingyi Hong ◽  
Xiangfeng Wang ◽  
Meisam Razaviyayn ◽  
Zhi-Quan Luo

2019 ◽  
Vol 41 (1) ◽  
pp. C1-C27 ◽  
Author(s):  
Aditya Devarakonda ◽  
Kimon Fountoulakis ◽  
James Demmel ◽  
Michael W. Mahoney

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