Two-Dimensional Data Partitioning for Non-negative Matrix Tri-Factorization

Jiaxing YAN, Hai LIU, Zhiqi LEI, Yanghui RAO, Guan LIU*, Haoran XIE, Xiaohui TAO, Fu lee WANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


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.
Original languageEnglish
Article number100473
JournalBig Data Research
Early online date19 Jun 2024
Publication statusE-pub ahead of print - 19 Jun 2024

Bibliographical note

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© 2024 Elsevier Inc.


  • 2-Dimensional data partitioning
  • Non-negative matrix tri-factorization
  • Text co-clustering


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