252 citations · 616 across the 34 of their papers we have counts for
23 papers · 1 filter
Missing Pattern Tree based Decision Grouping and Ensemble for Enhancing Pair Utilization in Deep Incomplete Multi-View Clustering
Jie Xu, Wenyuan Yang, Yazhou Ren +3
Real-world multi-view data often exhibit highly inconsistent missing patterns, posing significant challenges for incomplete multi-view clustering (IMVC). Although existing IMVC met…
A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective
Chenhang Cui, Yazhou Ren, Jingyu Pu +5
Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consisten…
Federated Deep Multi-View Clustering with Global Self-Supervision
Xinyue Chen, Jie Xu, Yazhou Ren +5
Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown an…
Deep Multi-View Subspace Clustering with Anchor Graph
Chenhang Cui, Yazhou Ren, Jingyu Pu +2
Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1)…
Deep Clustering: A Comprehensive Survey
Yazhou Ren, Jingyu Pu, Zhimeng Yang +5
Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clusterin…
Variational Graph Generator for Multi-View Graph Clustering
Jianpeng Chen, Yawen Ling, Jie Xu +6
Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to…
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs
Zongmo Huang, Yazhou Ren, Xiaorong Pu +1
With the representation learning capability of the deep learning models, deep embedded multi-view clustering (MVC) achieves impressive performance in many scenarios and has become…
Deep reinforcement learning guided graph neural networks for brain network analysis
Xusheng Zhao, Jia Wu, Hao Peng +9
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or co…
Multi-level Feature Learning for Contrastive Multi-view Clustering
Jie Xu, Huayi Tang, Yazhou Ren +3
Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same featur…
A Robust and Generalized Framework for Adversarial Graph Embedding
Jianxin Li, Xingcheng Fu, Hao Peng +5
Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-qua…
Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
Mehrnaz Najafi, Lifang He, Philip S. Yu
With recent advances in data collection from multiple sources, multi-view data has received significant attention. In multi-view data, each view represents a different perspective…
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Gongxu Luo, Jianxin Li, Jianlin Su +5
Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate…
Non-Linear Fusion for Self-Paced Multi-View Clustering
Zongmo Huang, Yazhou Ren, Xiaorong Pu +1
With the advance of the multi-media and multi-modal data, multi-view clustering (MVC) has drawn increasing attentions recently. In this field, one of the most crucial challenges is…
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Jianxin Li, Hao Peng, Yuwei Cao +4
Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis ta…
Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
Jie Xu, Yazhou Ren, Huayi Tang +6
Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the…
Boosted Sparse and Low-Rank Tensor Regression
Lifang He, Kun Chen, Wanwan Xu +2
We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coeffic…
A Self-Organizing Tensor Architecture for Multi-View Clustering
Lifang He, Chun-ta Lu, Yong Chen +4
In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although sev…
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction
Jianguo Zhang, Ji Wang, Lifang He +2
With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning…
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Ye Liu, Lifang He, Bokai Cao +3
Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a…
Error-Robust Multi-View Clustering
Mehrnaz Najafi, Lifang He, Philip S. Yu
In the era of big data, data may come from multiple sources, known as multi-view data. Multi-view clustering aims at generating better clusters by exploiting complementary and cons…
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Guixiang Ma, Chun-Ta Lu, Lifang He +2
Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common n…
Online Unsupervised Multi-view Feature Selection
Weixiang Shao, Lifang He, Chun-Ta Lu +2
In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabel…
Multi-Source Multi-View Clustering via Discrepancy Penalty
Weixiang Shao, Jiawei Zhang, Lifang He +1
With the advance of technology, entities can be observed in multiple views. Multiple views containing different types of features can be used for clustering. Although multi-view cl…