Tri-regularized nonnegative matrix tri-factorization for co-clustering

The objective of co-clustering is to simultaneously identify blocks of similarity between the sample set and feature set. Co-clustering has become a widely used technique in data mining, machine learning, and other research areas. The nonnegative matrix tri-factorization (NMTF) algorithm, which aims...

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Bibliographic Details
Published in:	Knowledge-based systems Vol. 226; p. 107101
Main Authors:	Deng, Ping, Li, Tianrui, Wang, Hongjun, Horng, Shi-Jinn, Yu, Zeng, Wang, Xiaomin
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 17.08.2021 Elsevier Science Ltd
Subjects:	Algorithms Clustering Co-clustering Data mining Eigenvalues Entrywise norm Factorization Grammatical aspect Graph regularization Information retrieval Iterative methods Machine learning Matrices Noise Noise sensitivity Nonnegative matrix tri-factorization Objectives Optimization Performance enhancement Regularization Sparsity Nonnegative matrix tri-factorization Graph regularization Sparsity Entrywise norm Co-clustering
ISSN:	0950-7051, 1872-7409
Online Access:	Get full text
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