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

• Special Issue: Machine Learning and Optimization Algorithm • Previous Articles     Next Articles

Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization

Yong-Yong Chen, Fang-Fang Xu   

  1. 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)

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

CLC Number: