Two Efficient Algorithms for Orthogonal Nonnegative Matrix Factorization. (1st July 2021)

• Record Type: Journal Article
• Authors: Wu, Jing; Chen, Bin; Han, Tao
• Other Names: Stanimirović Predrag S. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-07-01
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/8490147
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Nonnegative matrix factorization (NMF) is a popular method for the multivariate analysis of nonnegative data. It involves decomposing a data matrix into a product of two factor matrices with all entries restricted to being nonnegative. Orthogonal nonnegative matrix factorization (ONMF) has been introduced recently. This method has demonstrated remarkable performance in clustering tasks, such as gene expression classification. In this study, we introduce two convergence methods for solving ONMF. First, we design a convergent orthogonal algorithm based on the Lagrange multiplier method. Second, we propose an approach that is based on the alternating direction method. Finally, we demonstrate that the two proposed approaches tend to deliver higher-quality solutions and perform better in clustering tasks compared with a state-of-the-art ONMF.