248 citations · 323 across the 6 of their papers we have counts for
6 papers
CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network
Jie Wen, Zheng Zhang, Yong Xu +3
In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a…
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data
Yiming Wang, Dongxia Chang, Zhiqiang Fu +2
Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete…
A Survey on Incomplete Multi-view Clustering
Jie Wen, Zheng Zhang, Lunke Fei +4
Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, suc…
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering
Yiming Wang, Dongxia Chang, Zhiqiang Fu +2
In this paper, we propose an augmentation-free graph contrastive learning framework, namely ACTIVE, to solve the problem of partial multi-view clustering. Notably, we suppose that…
Incomplete Multi-view Clustering via Cross-view Relation Transfer
Yiming Wang, Dongxia Chang, Zhiqiang Fu +1
In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty…
Vehicle Re-identification Based on Dual Distance Center Loss
Zhijun Hu, Yong Xu, Jie Wen +2
Recently, deep learning has been widely used in the field of vehicle re-identification. When training a deep model, softmax loss is usually used as a supervision tool. However, the…