Incomplete Multiview Clustering Using Normalizing Alignment Strategy With Graph Regularization

Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 35; číslo 8; s. 8126 - 8142
Hlavní autori:	Cui, Guosheng, Wang, Ruxin, Wu, Dan, Li, Ye
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Alignment Centroids Clustering Convergence Factorization Feature alignment incomplete multiview Linear programming Mathematical analysis Matrix decomposition Microwave integrated circuits normalizing strategy Optimization pairwise co-regularization Predictive models Regularization Source code Task analysis Vector spaces
ISSN:	1041-4347, 1558-2191
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Abstract	Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data well. Addressing these issues, we propose a novel IMC algorithm using a normalizing alignment strategy (IMCNAS) based on nonnegative matrix factorization. Specifically, the columns of the basis matrices are constrained into unit vector space, which integrates the feature normalization and the optimizing process, and makes the model converge fast and stable. On the other hand, this enables the model to fit the data better and produce more reasonable factorization results. Further, we develop a novel pairwise co-regularization to align incomplete multiple views more directly, without introducing a common consensus matrix like traditional centroid-based co-regularization. Graph regularization is also incorporated in the proposed model to utilize the geometrical information of data. We implement IMCNAS with a centroid-based regularization and a pairwise co-regularization respectively, and leads to two variants, i.e., IMCNAS-1 and IMCNAS-2. Both variants are optimized with multiplicative updating rules. Extensive experiments conducted on various real-world datasets comparing several state-of-the-art IMC methods verified the effectiveness of the proposed methods. The source code is available at: https://github.com/GuoshengCui/IMCNAS .
AbstractList	Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data well. Addressing these issues, we propose a novel IMC algorithm using a normalizing alignment strategy (IMCNAS) based on nonnegative matrix factorization. Specifically, the columns of the basis matrices are constrained into unit vector space, which integrates the feature normalization and the optimizing process, and makes the model converge fast and stable. On the other hand, this enables the model to fit the data better and produce more reasonable factorization results. Further, we develop a novel pairwise co-regularization to align incomplete multiple views more directly, without introducing a common consensus matrix like traditional centroid-based co-regularization. Graph regularization is also incorporated in the proposed model to utilize the geometrical information of data. We implement IMCNAS with a centroid-based regularization and a pairwise co-regularization respectively, and leads to two variants, i.e., IMCNAS-1 and IMCNAS-2. Both variants are optimized with multiplicative updating rules. Extensive experiments conducted on various real-world datasets comparing several state-of-the-art IMC methods verified the effectiveness of the proposed methods. The source code is available at: https://github.com/GuoshengCui/IMCNAS .
Author	Li, Ye Cui, Guosheng Wang, Ruxin Wu, Dan
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SubjectTerms	Algorithms Alignment Centroids Clustering Convergence Factorization Feature alignment incomplete multiview Linear programming Mathematical analysis Matrix decomposition Microwave integrated circuits normalizing strategy Optimization pairwise co-regularization Predictive models Regularization Source code Task analysis Vector spaces
Title	Incomplete Multiview Clustering Using Normalizing Alignment Strategy With Graph Regularization
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