Parallel non-negative matrix tri-factorization for text data co-clustering

Yufu CHEN, Zhiqi LEI, Yanghui RAO*, Haoran XIE, Fu Lee WANG, Jian YIN, Qing LI

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

As a novel paradigm for data mining and dimensionality reduction, Non-negative Matrix Tri-Factorization (NMTF) has attracted much attention due to its notable performance and elegant mathematical derivation, and it has been applied to a plethora of real-world applications, such as text data co-clustering. However, the existing NMTF-based methods usually involve intensive matrix multiplications, which exhibits a major limitation of high computational complexity. With the explosion at both the size and the feature dimension of texts, there is a growing need to develop a parallel and scalable NMTF-based algorithm for text data co-clustering. To this end, we first show in this paper how to theoretically derive the original optimization problem of NMTF by introducing the Lagrangian multipliers. Then, we propose to solve the Lagrange dual objective function in parallel through an efficient distributed implementation. Extensive experiments on five benchmark corpora validate the effectiveness, efficiency, and scalability of our distributed parallel update algorithm for an NMTF-based text data co-clustering method.
Original languageEnglish
Pages (from-to)5132-5146
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume35
Issue number5
Early online date31 Jan 2022
DOIs
Publication statusPublished - 1 May 2023

Bibliographical note

Funding Information:
The work of Yanghui Rao was supported in part by the National Natural Science Foundation of China under Grant 61972426 and in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515010536. The work of Haoran Xie was supported in part by the Direct under Grant DR22A2 and in part by the Faculty Research under Grants DB22A5 and DB21A9 of Lingnan University, Hong Kong. The work of Jian Yin was supported in part by theNationalNatural Science Foundation of China under Grants U1811264, U1811262, U1811261, U1911203, U2001211, U1711262, and U1711261, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019B1515130001, and in part by the Key-Area Research and Development Program of Guangdong Province under Grants 2018B010107005 and 2020B0101100001. The work of Qing Li was supported in part by theHong Kong ResearchGrants Council under a Collaborative Research Fund under Grant C1031-18G.

Publisher Copyright:
© 1989-2012 IEEE.

Keywords

  • Non-negative matrix tri-factorization
  • parallel computing
  • message passing
  • Newton iteration
  • Computational modeling
  • Scalability
  • Data models
  • Partitioning algorithms
  • Matrix decomposition
  • Optimization
  • Convergence

Fingerprint

Dive into the research topics of 'Parallel non-negative matrix tri-factorization for text data co-clustering'. Together they form a unique fingerprint.

Cite this