RicciNet: Deep Clustering via A Riemannian Generative Model-Reference-Cited by-同舟云学术

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RicciNet: Deep Clustering via A Riemannian Generative Model

Published:2024-05-13 Issue: Volume:119 Page:4071-4082
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Sun Li¹^ORCID,Hu Jingbin¹^ORCID,Zhou Suyang¹^ORCID,Huang Zhenhao¹^ORCID,Ye Junda²^ORCID,Peng Hao³^ORCID,Yu Zhengtao⁴^ORCID,Yu Philip⁵^ORCID

Affiliation:

1. North China Electric Power University, Beijing, China

2. Beijing University of Posts and Telecommunications, Beijing, China

3. Beihang University, Beijing, China

4. Kunming University of Science and Technology, Kunming, China

5. University of Illinois, Chicago, Chicago, IL, USA

Funder

NSFC

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645428

Reference55 articles.

1. Gregor Bachmann, Gary Bécigneul, and Octavian Ganea. 2020. Constant Curvature Graph Convolutional Networks. In Proceedings of the 37th ICML, Vol. 119. 486--496.

2. Gary Bécigneul and Octavian-Eugen Ganea. 2019. Riemannian Adaptive Optimization Methods. In Proceedings of 7th ICLR.

3. Structural Deep Clustering Network

4. Stochastic Gradient Descent on Riemannian Manifolds

5. Efficient Deep Embedded Subspace Clustering

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