Deep image clustering with contrastive learning and multi-scale graph convolutional networks

Author:

Xu Yuankun,Huang DongORCID,Wang Chang-DongORCID,Lai Jian-Huang

Funder

Natural Science Foundation of Guangdong Province

DCMS NSFC PKU

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference54 articles.

1. Joint multi-view unsupervised feature selection and graph learning;Fang;IEEE Trans. Emerg. Top. Comput. Intell.,2023

2. Region-aware hierarchical latent feature representation learning-guided clustering for hyperspectral band selection;Wang;IEEE Trans. Cybern.,2022

3. Towards scalable multi-view clustering via joint learning of many bipartite graphs;Lao;IEEE Trans. Big Data,2023

4. Fast multi-view clustering via ensembles: Towards scalability, superiority, and simplicity;Huang;IEEE Trans. Knowl. Data Eng.,2023

5. B. Yang, X. Fu, N.D. Sidiropoulos, M. Hong, Towards k-means-friendly spaces: Simultaneous deep learning and clustering, in: Proceedings of the International Conference on Machine Learning, 2017, pp. 3861–3870.

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