no code implementations • 28 Jan 2024 • Zisen Kong, Zhiqiang Fu, Dongxia Chang, Yiming Wang, Yao Zhao
We jointly optimize the construction of the latent consistent anchor graph and the feature transformation to generate a discriminative anchor graph.
no code implementations • 8 Nov 2022 • Yiming Wang, Dongxia Chang, Zhiqiang Fu, Jie Wen, Yao Zhao
Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields.
no code implementations • 1 Mar 2022 • Yiming Wang, Dongxia Chang, Zhiqiang Fu, Jie Wen, Yao Zhao
In this paper, we propose an augmentation-free graph contrastive learning framework, namely ACTIVE, to solve the problem of partial multi-view clustering.
no code implementations • 1 Dec 2021 • Yiming Wang, Dongxia Chang, Zhiqiang Fu, Yao Zhao
In this paper, we consider the problem of multi-view clustering on incomplete views.
no code implementations • CVPR 2021 • Zhiqiang Fu, Yao Zhao, Dongxia Chang, Xingxing Zhang, Yiming Wang
This paper presents a novel, simple yet robust self-representation method, i. e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering.
no code implementations • 11 May 2021 • Yiming Wang, Dongxia Chang, Zhiqiang Fu, Yao Zhao
Specifically, a multiple graph auto-encoder(M-GAE) is designed to flexibly encode the complementary information of multi-view data using a multi-graph attention fusion encoder.
no code implementations • 26 Apr 2021 • Zhiqiang Fu, Yao Zhao, Dongxia Chang, Xingxing Zhang, Yiming Wang
In this paper, a novel unsupervised low-rank representation model, i. e., Auto-weighted Low-Rank Representation (ALRR), is proposed to construct a more favorable similarity graph (SG) for clustering.