Two-dimensional data partitioning for non-negative matrix tri-factorization
Article
Yan, Jiaxing, Liu, Hai, Lei, Zhiqi, Rao, Yanghui, Liu, Guan, Xie, Haoran, Tao, Xiaohui and Wang, Fu Lee. 2024. "Two-dimensional data partitioning for non-negative matrix tri-factorization." Big Data Research. 37. https://doi.org/10.1016/j.bdr.2024.100473
Article Title | Two-dimensional data partitioning for non-negative matrix tri-factorization |
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ERA Journal ID | 210189 |
Article Category | Article |
Authors | Yan, Jiaxing, Liu, Hai, Lei, Zhiqi, Rao, Yanghui, Liu, Guan, Xie, Haoran, Tao, Xiaohui and Wang, Fu Lee |
Journal Title | Big Data Research |
Journal Citation | 37 |
Article Number | 100473 |
Number of Pages | 9 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 2214-5796 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bdr.2024.100473 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S2214579624000492 |
Abstract | As a two-sided clustering and dimensionality reduction paradigm, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention in machine learning and data mining researchers due to its excellent performance and reliable theoretical support. Unlike Non-negative Matrix Factorization (NMF) methods applicable to one-sided clustering only, NMTF introduces an additional factor matrix and uses the inherent duality of data to realize the mutual promotion of sample clustering and feature clustering, thus showing great advantages in many scenarios (e.g., text co-clustering). However, the existing methods for solving NMTF usually involve intensive matrix multiplication, which is characterized by high time and space complexities, that is, there are limitations of slow convergence of the multiplicative update rules and high memory overhead. In order to solve the above problems, this paper develops a distributed parallel algorithm with a 2-dimensional data partition scheme for NMTF (i.e., PNMTF-2D). Experiments on multiple text datasets show that the proposed PNMTF-2D can substantially improve the computational efficiency of NMTF (e.g., the average iteration time is reduced by up to 99.7% on Amazon) while ensuring the effectiveness of convergence and co-clustering. |
Keywords | 2-Dimensional data partitioning; Non-negative matrix tri-factorization; Text co-clustering |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Sun Yat-sen University, China |
Guangdong Power Grid Corporation, China | |
Jinan University, China | |
Lingnan University of Hong Kong, China | |
School of Sciences | |
Hong Kong Metropolitan University, China |
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