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- Title
Robust multi-view low-rank embedding clustering.
- Authors
Dai, Jian; Song, Hong; Luo, Yunzhi; Ren, Zhenwen; Yang, Jian
- Abstract
Significant improvements of multi-view subspace clustering have emerged in recent years. However, multi-view data are often lying on high-dimensional space and inevitably corrupted by noise and even outliers, which pose challenges for fully exploiting the intrinsic underlying relevance of multi-view data, as the redundant and corrupted features are highly deceptive. To address the above problems, this paper proposes a robust multi-view low-rank embedding (RMLE) method for clustering. Specifically, RMLE projects each high-dimensional view onto a clean low-rank embedding space without energy loss, such that multiple high-quality candidate affinity graphs are yielded by using self-expressiveness subspace learning. Meanwhile, it integrates the clean complimentary information of multi-view data in semantic space to learn a shared consensus affinity graph. Further, an efficient alternating optimization algorithm is designed to solve our RMLE by the alternating direction method of multipliers. Extensive experiments on four benchmark multi-view datasets demonstrate the performance superiority and advantages of RMLE against many state-of-the-art clustering methods.
- Subjects
ENERGY dissipation; MULTIPLIERS (Mathematical analysis); MATHEMATICAL optimization
- Publication
Neural Computing & Applications, 2023, Vol 35, Issue 10, p7877
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-022-08137-w