305 citations · 1k across the 30 of their papers we have counts for
15 papers · 1 filter
InfMasking: Unleashing Synergistic Information by Contrastive Multimodal Interactions
Liangjian Wen, Qun Dai, Jianzhuang Liu +7
In multimodal representation learning, synergistic interactions between modalities not only provide complementary information but also create unique outcomes through specific inter…
High-order Multi-view Clustering for Generic Data
Erlin Pan, Zhao Kang
Graph-based multi-view clustering has achieved better performance than most non-graph approaches. However, in many real-world scenarios, the graph structure of data is not given or…
Structure-Preserving Graph Representation Learning
Ruiyi Fang, Liangjian Wen, Zhao Kang +1
Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an…
Scalable Multi-view Clustering with Graph Filtering
Liang Liu, Peng Chen, Guangchun Luo +3
With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space a…
Multi-view Contrastive Graph Clustering
Erlin Pan, Zhao Kang
With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either fo…
Self-paced Principal Component Analysis
Zhao Kang, Hongfei Liu, Jiangxin Li +2
Principal Component Analysis (PCA) has been widely used for dimensionality reduction and feature extraction. Robust PCA (RPCA), under different robust distance metrics, such as l1-…
On Deep Unsupervised Active Learning
Changsheng Li, Handong Ma, Zhao Kang +3
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.…
Relation-Guided Representation Learning
Zhao Kang, Xiao Lu, Jian Liang +2
Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most do…
Large-scale Multi-view Subspace Clustering in Linear Time
Zhao Kang, Wangtao Zhou, Zhitong Zhao +3
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of vie…
Multiple Partitions Aligned Clustering
Zhao Kang, Zipeng Guo, Shudong Huang +4
Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering m…
Clustering with Similarity Preserving
Zhao Kang, Honghui Xu, Boyu Wang +2
Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel…
Low-rank Kernel Learning for Graph-based Clustering
Zhao Kang, Liangjian Wen, Wenyu Chen +1
Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. Howev…
Similarity Learning via Kernel Preserving Embedding
Zhao Kang, Yiwei Lu, Yuanzhang Su +2
Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning…
Unified Spectral Clustering with Optimal Graph
Zhao Kang, Chong Peng, Qiang Cheng +1
Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous l…
Twin Learning for Similarity and Clustering: A Unified Kernel Approach
Zhao Kang, Chong Peng, Qiang Cheng
Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is c…