Abstract
Incomplete multi-view clustering is an important research topic in multimedia where partial data entries of one or more views are missing. Current subspace clustering approaches mostly employ matrix factorization on the observed feature matrices to address this issue. Meanwhile, self-representation technique is left unexplored, since it explicitly relies on full data entries to construct the coefficient matrix, which is contradictory to the incomplete data setting. However, it is widely observed that self-representation subspace method enjoys a better clustering performance over the factorization based one. Therefore, we adapt it to incomplete data by jointly performing data imputation and self-representation learning. To the best of our knowledge, this is the first attempt in incomplete multi-view clustering literature. Besides, the proposed method is carefully compared with current advances in experiment with respect to different missing ratios, verifying its effectiveness.
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 | 2726-2734 |
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
62006236), Education Ministry-China Mobile Research Funding (No. MCM20170404), Hunan Provincial Natural Science Foundation (No. 2020JJ5673) and NUDT Research Project (No. ZK20-10). The work is supported by National Key R&D Program of China (No. 2020AAA0107100), National Natural Science Foundation of China (No. 61922088, 61773392, 61872377, 61976196, 62006237 and
Keywords
- incomplete data
- multi-view clustering
- self-representation learning
- subspace clustering