Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization

doi:10.1007/s40305-020-00322-9

Journal of the Operations Research Society of China ›› 2023, Vol. 11 ›› Issue (2): 327-345.doi: 10.1007/s40305-020-00322-9

收稿日期:2019-11-26 修回日期:2020-06-07 出版日期:2023-06-30 发布日期:2023-05-24
通讯作者: Fang-Fang Xu, Yong-Yong Chen E-mail:xuff@sdust.edu.cn;yongyongchen.cn@gmail.com

Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization

Yong-Yong Chen, Fang-Fang Xu

College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, Shandong, China

Received:2019-11-26 Revised:2020-06-07 Online:2023-06-30 Published:2023-05-24
Contact: Fang-Fang Xu, Yong-Yong Chen E-mail:xuff@sdust.edu.cn;yongyongchen.cn@gmail.com
Supported by:
the National Natural Science Foundation of China (No. 11901359), and Shandong Provincial Natural Science Foundation (No. ZR2019QA017)

摘要/Abstract

Abstract: Orthogonal nonnegative matrix factorization (ONMF) is widely used in blind image separation problem, document classification, and human face recognition. The model of ONMF can be efficiently solved by the alternating direction method of multipliers and hierarchical alternating least squares method. When the given matrix is huge, the cost of computation and communication is too high. Therefore, ONMF becomes challenging in the large-scale setting. The random projection is an efficient method of dimensionality reduction. In this paper, we apply the random projection to ONMF and propose two randomized algorithms. Numerical experiments show that our proposed algorithms perform well on both simulated and real data.

Key words: Orthogonal nonnegative matrix factorization, Random projection method, Dimensionality reduction, Augmented lagrangian method, Hierarchical alternating least squares algorithm

中图分类号:

68W20
90C06

. [J]. Journal of the Operations Research Society of China, 2023, 11(2): 327-345.

Yong-Yong Chen, Fang-Fang Xu. Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization[J]. Journal of the Operations Research Society of China, 2023, 11(2): 327-345.

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