Abstract
Multiview clustering is to more fully use the information between views to guide the division of data points, and multiview data is often accompanied by high-dimensionality. Since non-negative matrix factorization can effectively extract features while reducing dimensionality, this paper proposed a multi-view learning method based on non-negative matrix factorization. Compared with other NMF-based multiview learning methods, the proposed method has the following advantages: 1) graph regularization is added to traditional NMF to explore potential popular structures, so that the learned similarity graph contains more potential information. 2) A common graph learning strategy is designed to integrate hidden information from different views. 3) Put the NMF-based similarity graph learning and common graph learning strategies into a unified framework, and optimize the similarity graph and common graph at the same time, so that the two promote each other. Experiments on three public datasets show that the proposed method is more robust than the existing methods.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 62071157, Natural Science Foundation of Heilongjiang Province under Grant YQ2019F011 and Postdoctoral Foundation of Heilongjiang Province under Grant LBH-Q19112.
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Chen, J., Li, A., Li, J., Wang, Y. (2022). Multiview Learning via Non-negative Matrix Factorization for Clustering Applications. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_31
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DOI: https://doi.org/10.1007/978-3-031-04245-4_31
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