Special Issue: Machine Learning and Optimization Algorithm

Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization

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

Received date: 2019-11-26

  Revised date: 2020-06-07

  Online published: 2023-05-24

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.

Cite this article

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 . DOI: 10.1007/s40305-020-00322-9

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