Paper Info
Title
Semi-supervised multi-view clustering with Graph-regularized Partially Shared Non-negative Matrix Factorization
Abstract
Non-negative matrix factorization is widely used in multi-view clustering due to its ability of learning a common dimension-reduced factor. Recently, it is combined with the label information to improve the clustering, but the affection of the dimension-reduction to the classes of the labeled data is seldom considered. Motivated by that the graph constraint can keep the geometric structure of the data, it is employed to restrict the class variation of the data caused by the dimension reduction, and a semi-supervised method called Graph-regularized Partially Shared Non-negative Matrix Factorization (GPSNMF) is proposed for multi-view clustering in this paper. In our method, the affinity graph of each view is constructed to encode the geometric information, and the corresponding multiplication update algorithm based on alternative iteration rule is derived. In the experiments, two clustering approaches are tested based on the results of the proposed GPSNMF, and four real-world databases with different label proportions are performed to demonstrate the advantages of our method over the state-of-the-art methods.
Year
DOI
Venue
2020
10.1016/j.knosys.2019.105185
Knowledge-Based Systems
Keywords
Field
DocType
Graph-regularization,Semi-supervised learning,Multi-view clustering,Non-negative matrix factorization
ENCODE,Data mining,Graph,Dimensionality reduction,Pattern recognition,Computer science,Matrix decomposition,Multiplication,Artificial intelligence,Non-negative matrix factorization,Labeled data,Cluster analysis
Journal
Volume
ISSN
Citations 
190
0950-7051
5
PageRank 
References 
Authors
0.40
0
5
Name
Order
Citations
PageRank
Naiyao Liang1212.59
Zu-yuan Yang231224.12
Zhenni Li39914.48
Shengli Xie42530161.51
Chun-Yi Su52681209.14