Multi-view Subspace Clustering Based on Unified Measure Standard
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-022-11136-6.pdf
Reference48 articles.
1. Vidal R (2011) Subspace clustering. IEEE Signal Process Mag 28(2):52–68
2. Li B, Liu R, Cao J, Zhang J, Lai Y, Liu X (2018) Online low-rank representation learning for joint multi-subspace recovery and clustering. IEEE Trans Image Process 27(1):335–348
3. Peng X, Feng J, Xiao S, Yau W, Zhou JT, Yang S (2018) Structured autoencoders for subspace clustering. IEEE Trans Image Process 27(10):5076–5086
4. Wang B, Hu Y, Gao J, Sun Y, Ju F, Yin B (2021) Learning adaptive neighborhood graph on Grassmann manifolds for video/image-set subspace clustering. IEEE Trans Multimed 23:216–227
5. Yin M, Liu W, Li M, Jin T, Ji R (2021) Cauchy loss induced block diagonal representation for robust multi-view subspace clustering. Neurocomputing 427:84–95
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