Abstract
Multi-view clustering (MVC) has garnered significant attention in recent studies. In this paper, we propose a novel MVC method, named CCL-MVC. The novel method constructs a cross-order neighbor tensor of multi-view data to recover a low-rank essential tensor, which preserves noise-free, comprehensive, and complementary cross-order relationships among the samples. Furthermore, it constructs a consensus representation matrix by fusing the low-rank essential tensor with auto-adjusted cross-view diversity embedding, fully exploiting both consensus and discriminative information of the data. An effective optimization algorithm is developed, which is theoretically guaranteed to converge. Extensive experimental results confirm the effectiveness of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
| Editors | Kate Larson |
| Pages | 4788-4796 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781956792041 |
| State | Published - 2024 |
| Event | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 - Jeju, Korea, Republic of Duration: Aug 3 2024 → Aug 9 2024 |
Publication series
| Name | IJCAI International Joint Conference on Artificial Intelligence |
|---|---|
| ISSN (Print) | 1045-0823 |
Conference
| Conference | 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Jeju |
| Period | 8/3/24 → 8/9/24 |
Bibliographical note
Publisher Copyright:© 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.
Funding
Y.C. and C.C. are corresponding authors. This work is supported in part by the National Natural Science Foundation of China (NSFC) under Grants 62276147, 62172246, and 62106063; in part by the Shandong Province Colleges and Universities Youth Innovation Technology Plan Innovation Team Project under Grants 2022KJ149, 2021KJ062, and 2020KJN011; and in part by the Guangdong Major Project of Basic and Applied Basic Research under Grant 2023B0303000010.
| Funders | Funder number |
|---|---|
| Shandong Province Colleges and Universities Youth Innovation Technology Plan Innovation Team Project, China | 2020KJN011, 2022KJ149, 2021KJ062 |
| National Natural Science Foundation of China (NSFC) | 62106063, 62276147, 62172246 |
| National Natural Science Foundation of China (NSFC) | |
| Guangdong Major Project of Basic and Applied Basic Research | 2023B0303000010 |
ASJC Scopus subject areas
- Artificial Intelligence