Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering
Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4083 |
| _version_ | 1797575998153162752 |
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| author | Geng Yang Qin Li Yu Yun Yu Lei Jane You |
| author_facet | Geng Yang Qin Li Yu Yun Yu Lei Jane You |
| author_sort | Geng Yang |
| collection | DOAJ |
| description | Graph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods. |
| first_indexed | 2024-03-10T21:47:06Z |
| format | Article |
| id | doaj.art-d4476d57756f47bbb29c26b440b5c8be |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:47:06Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d4476d57756f47bbb29c26b440b5c8be2023-11-19T14:16:59ZengMDPI AGElectronics2079-92922023-09-011219408310.3390/electronics12194083Hypergraph Learning-Based Semi-Supervised Multi-View Spectral ClusteringGeng Yang0Qin Li1Yu Yun2Yu Lei3Jane You4School of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaSchool of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaSchool of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaSchool of Software Engineering, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaDepartment of Computing, The Hong Kong Polytechnic University, Hong Kong 100872, ChinaGraph-based semi-supervised multi-view clustering has demonstrated promising performance and gained significant attention due to its capability to handle sample spaces with arbitrary shapes. Nevertheless, the ordinary graph employed by most existing semi-supervised multi-view clustering methods only captures the pairwise relationships between samples, and cannot fully explore the higher-order information and complex structure among multiple sample points. Additionally, most existing methods do not make full use of the complementary information and spatial structure contained in multi-view data, which is crucial to clustering results. We propose a novel hypergraph learning-based semi-supervised multi-view spectral clustering approach to overcome these limitations. Specifically, the proposed method fully considers the relationship between multiple sample points and utilizes hypergraph-induced hyper-Laplacian matrices to preserve the high-order geometrical structure in data. Based on the principle of complementarity and consistency between views, this method simultaneously learns indicator matrices of all views and harnesses the tensor Schatten p-norm to extract both complementary information and low-rank spatial structure within these views. Furthermore, we introduce an auto-weighted strategy to address the discrepancy between singular values, enhancing the robustness and stability of the algorithm. Detailed experimental results on various datasets demonstrate that our approach surpasses existing state-of-the-art semi-supervised multi-view clustering methods.https://www.mdpi.com/2079-9292/12/19/4083semi-supervised learningmulti-view clusteringhypergraph learning |
| spellingShingle | Geng Yang Qin Li Yu Yun Yu Lei Jane You Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering Electronics semi-supervised learning multi-view clustering hypergraph learning |
| title | Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering |
| title_full | Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering |
| title_fullStr | Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering |
| title_full_unstemmed | Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering |
| title_short | Hypergraph Learning-Based Semi-Supervised Multi-View Spectral Clustering |
| title_sort | hypergraph learning based semi supervised multi view spectral clustering |
| topic | semi-supervised learning multi-view clustering hypergraph learning |
| url | https://www.mdpi.com/2079-9292/12/19/4083 |
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