104 citations · 185 across the 9 of their papers we have counts for
9 papers
Association and Consolidation: Evolutionary Memory-Enhanced Incremental Multi-View Clustering
Zisen Kong, Bo Zhong, Pengyuan Li +3
Incremental multi-view clustering aims to achieve stable clustering results while addressing the stability-plasticity dilemma (SPD) in view-incremental scenarios. The core challeng…
Revisiting Radar Camera Alignment by Contrastive Learning for 3D Object Detection
Linhua Kong, Dongxia Chang, Lian Liu +3
Recently, 3D object detection algorithms based on radar and camera fusion have shown excellent performance, setting the stage for their application in autonomous driving perception…
One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering
Zisen Kong, Zhiqiang Fu, Dongxia Chang +2
In this paper, we propose a novel multi-view clustering model, named Dual-space Co-training Large-scale Multi-view Clustering (DSCMC). The main objective of our approach is to enha…
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…
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…
Seeing All From a Few: Nodes Selection Using Graph Pooling for Graph Clustering
Yiming Wang, Dongxia Chang, Zhiqian Fu +1
Recently, there has been considerable research interest in graph clustering aimed at data partition using the graph information. However, one limitation of the most of graph-based…
Consistent Multiple Graph Embedding for Multi-View Clustering
Yiming Wang, Dongxia Chang, Zhiqiang Fu +1
Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been…
Auto-weighted low-rank representation for clustering
Zhiqiang Fu, Yao Zhao, Dongxia Chang +2
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 (S…