Abstract
Multi-view subspace clustering has received widespread attention to effectively fuse multi-view information among multimedia applications. Considering that most existing approaches' cubic time complexity makes it challenging to apply to realistic large-scale scenarios, some researchers have addressed this challenge by sampling anchor points to capture distributions in different views. However, the separation of the heuristic sampling and clustering process leads to weak discriminate anchor points. Moreover, the complementary multi-view information has not been well utilized since the graphs are constructed independently by the anchors from the corresponding views. To address these issues, we propose a Scalable Multi-view Subspace Clustering with Unified Anchors (SMVSC). To be specific, we combine anchor learning and graph construction into a unified optimization framework. Therefore, the learned anchors can represent the actual latent data distribution more accurately, leading to a more discriminative clustering structure. Most importantly, the linear time complexity of our proposed algorithm allows the multi-view subspace clustering approach to be applied to large-scale data. Then, we design a four-step alternative optimization algorithm with proven convergence. Compared with state-of-the-art multi-view subspace clustering methods and large-scale oriented methods, the experimental results on several datasets demonstrate that our SMVSC method achieves comparable or better clustering performance much more efficiently. The code of SMVSC is available at https://github.com/Jeaninezpp/SMVSC.
Original language | English |
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Title of host publication | MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia |
Publisher | Association for Computing Machinery, Inc |
Pages | 3528-3536 |
Number of pages | 9 |
ISBN (Electronic) | 9781450386517 |
DOIs | |
State | Published - Oct 17 2021 |
Externally published | Yes |
Event | 29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China Duration: Oct 20 2021 → Oct 24 2021 |
Publication series
Name | MM 2021 - Proceedings of the 29th ACM International Conference on Multimedia |
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Conference
Conference | 29th ACM International Conference on Multimedia, MM 2021 |
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Country/Territory | China |
City | Virtual, Online |
Period | 10/20/21 → 10/24/21 |
Funding
This work was supported by the National Key R&D Program of China 2020AAA0107100, the Natural Science Foundation of China (project no. 61872377, 61922088, 61773392, 61976196 and 62006237).
Keywords
- multi-view clustering
- scalable graph clustering
- subspace clustering