190 citations · 222 across the 14 of their papers we have counts for
14 papers
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning
Xihong Yang, Xiaochang Hu, Sihang Zhou +2
Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regulariz…
Deep Graph Clustering via Dual Correlation Reduction
Yue Liu, Wenxuan Tu, Sihang Zhou +4
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, w…
Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
Siqi Wang, Guang Yu, Zhiping Cai +3
Although deep neural networks (DNNs) enable great progress in video abnormal event detection (VAD), existing solutions typically suffer from two issues: (1) The localization of vid…
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment
Chen Zhang, Siwei Wang, Jiyuan Liu +5
Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to…
Multi-view Deep One-class Classification: A Systematic Exploration
Siqi Wang, Jiyuan Liu, Guang Yu +5
One-class classification (OCC), which models one single positive class and distinguishes it from the negative class, has been a long-standing topic with pivotal application to real…
Deep Distribution-preserving Incomplete Clustering with Optimal Transport
Mingjie Luo, Siwei Wang, Xinwang Liu +5
Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dr…
Deep Fusion Clustering Network
Wenxuan Tu, Sihang Zhou, Xinwang Liu +4
Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structu…
Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix
Weixuan Liang, Sihang Zhou, Jian Xiong +5
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views. Though demo…
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
Guang Yu, Siqi Wang, Zhiping Cai +4
As a vital topic in media content interpretation, video anomaly detection (VAD) has made fruitful progress via deep neural network (DNN). However, existing methods usually follow a…
SimpleMKKM: Simple Multiple Kernel K-means
Xinwang Liu, En Zhu, Jiyuan Liu +3
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment c…
Understand Dynamic Regret with Switching Cost for Online Decision Making
Yawei Zhao, Qian Zhao, Xingxing Zhang +3
As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear…
Simultaneous Clustering and Optimization for Evolving Datasets
Yawei Zhao, En Zhu, Xinwang Liu +3
Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solv…
Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit
Yawei Zhao, En Zhu, Xinwang Liu +1
We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the…
Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets
Yawei Zhao, Kai Xu, Xinwang Liu +3
Recently, network lasso has drawn many attentions due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data…