Kernelized multi-view subspace clustering via auto-weighted graph learning-Reference-Cited by-同舟云学术

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Kernelized multi-view subspace clustering via auto-weighted graph learning

Published:2021-05-10 Issue:1 Volume:52 Page:716-731
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zhang Guang-Yu,Chen Xiao-Wei,Zhou Yu-Ren,Wang Chang-Dong,Huang Dong,He Xiao-Yu

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-021-02365-8.pdf

Reference58 articles.

1. Huang S, Kang Z, Tsang IW, Xu Z (2019) Auto-weighted multi-view clustering via kernelized graph learning. Pattern Recogn 88:174–184

2. Chen MS, Huang L, Wang CD, Huang D (2020) Multi-view clustering in latent embedding space. In: Thirty-fourth AAAI conference on artificial intelligence

3. Gao H, Nie F, Li X, Huang H (2015) Multi-view subspace clustering. In: Proceedings of the IEEE international conference on computer vision, pp 4238–4246

4. Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4279–4287

5. Zhou T, Liu M, Thung K, Shen D (2019) Latent representation learning for alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans Medical Imaging 38(10):2411–2422

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