Multi-view data clustering via non-negative matrix factorization with manifold regularization

Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually, multi-view data have complementary information from various views. The main concern behind the NMF is how to factorize the data to achieve a significan...

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Vydané v:International journal of machine learning and cybernetics Ročník 13; číslo 3; s. 677 - 689
Hlavní autori: Khan, Ghufran Ahmad, Hu, Jie, Li, Tianrui, Diallo, Bassoma, Wang, Hongjun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2022
Springer Nature B.V
Predmet:
Algorithms
Approximation
Artificial Intelligence
Cluster analysis
Clustering
Complex Systems
Computational Intelligence
Control
Engineering
Factorization
Machine learning
Manifolds
Mechatronics
Optimization
Original Article
Pattern Recognition
Regularization
Robotics
Sparsity
Systems Biology
Multi-view clustering
Non-negative matrix factorization
Manifold regularization
Weighted view
ISSN:1868-8071, 1868-808X
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Shrnutí:Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually, multi-view data have complementary information from various views. The main concern behind the NMF is how to factorize the data to achieve a significant clustering solution from these complementary views. However, NMF does not focus to conserve the geometrical structures of the data space. In this article, we intensify on the above issue and evolve a new NMF clustering method with manifold regularization for multi-view data. The manifold regularization factor is exploited to retain the locally geometrical structure of the data space and gives extensively common clustering solution from multiple views. The weight control term is adopted to handle the distribution of each view weight. An iterative optimization strategy depended on multiplicative update rule is applied on the objective function to achieve optimization. Experimental analysis on the real-world datasets are exhibited that the proposed approach achieves better clustering performance than some state-of-the-art algorithms.
Bibliografia:ObjectType-Article-1
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-021-01307-7