Two-dimensional data partitioning for non-negative matrix tri-factorization

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)...

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Vydáno v:	Big data research Ročník 37; s. 100473
Hlavní autoři:	Yan, Jiaxing, Liu, Hai, Lei, Zhiqi, Rao, Yanghui, Liu, Guan, Xie, Haoran, Tao, Xiaohui, Wang, Fu Lee
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 28.08.2024
Témata:	2-Dimensional data partitioning Non-negative matrix tri-factorization Text co-clustering 2-Dimensional data partitioning Text co-clustering Non-negative matrix tri-factorization
ISSN:	2214-5796, 2214-580X
On-line přístup:	Získat plný text
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Shrnutí:	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.
ISSN:	2214-5796 2214-580X
DOI:	10.1016/j.bdr.2024.100473