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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| Veröffentlicht in: | Big data research Jg. 37; S. 100473 |
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28.08.2024
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 100473 |
| Author | Lei, Zhiqi Yan, Jiaxing Liu, Guan Rao, Yanghui Tao, Xiaohui Liu, Hai Xie, Haoran Wang, Fu Lee |
| Author_xml | – sequence: 1 givenname: Jiaxing surname: Yan fullname: Yan, Jiaxing email: yanjx6@mail2.sysu.edu.cn organization: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China – sequence: 2 givenname: Hai surname: Liu fullname: Liu, Hai email: liuh396@mail.sysu.edu.cn organization: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China – sequence: 3 givenname: Zhiqi surname: Lei fullname: Lei, Zhiqi email: leizhiqi01@126.com organization: Information Center, Guangdong Power Grid Corporation, Guangzhou, China – sequence: 4 givenname: Yanghui surname: Rao fullname: Rao, Yanghui email: raoyangh@mail.sysu.edu.cn organization: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China – sequence: 5 givenname: Guan orcidid: 0000-0002-1272-292X surname: Liu fullname: Liu, Guan email: liuguan@jnu.edu.cn organization: Computational Communication Studies, Jinan University, Guangzhou, China – sequence: 6 givenname: Haoran surname: Xie fullname: Xie, Haoran email: hrxie2@gmail.com organization: School of Data Science, Lingnan University, Tuen Mun, New Territories, Hong Kong Special Administrative Region of China – sequence: 7 givenname: Xiaohui surname: Tao fullname: Tao, Xiaohui email: xiaohui.tao@usq.edu.au organization: School of Sciences, University of Southern Queensland, Toowoomba, Australia – sequence: 8 givenname: Fu Lee surname: Wang fullname: Wang, Fu Lee email: pwang@hkmu.edu.hk organization: School of Science and Technology, Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong Special Administrative Region of China |
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| Cites_doi | 10.1109/TKDE.2012.51 10.1016/j.knosys.2021.107160 10.1109/TKDE.2017.2767592 10.1109/TKDE.2016.2606098 10.1109/TKDE.2023.3267496 10.1016/0167-8191(94)90028-0 10.1016/j.eswa.2017.01.019 10.1007/s10766-009-0116-7 10.1145/42411.42415 10.1142/S0129626416500146 10.1038/44565 10.1002/cpe.1206 |
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| Keywords | 2-Dimensional data partitioning Text co-clustering Non-negative matrix tri-factorization |
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year: 1994 ident: 10.1016/j.bdr.2024.100473_br0110 article-title: The mpi message passing interface standard – start-page: 635 year: 2005 ident: 10.1016/j.bdr.2024.100473_br0040 article-title: Co-clustering by block value decomposition – volume: 19 start-page: 1749 issue: 13 year: 2007 ident: 10.1016/j.bdr.2024.100473_br0150 article-title: Collective communication: theory, practice, and experience publication-title: Concurrency and Computation doi: 10.1002/cpe.1206 |
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| Title | Two-dimensional data partitioning for non-negative matrix tri-factorization |
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