Consensus graph and spectral representation for one-step multi-view kernel based clustering
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Consensus graph and spectral representation for one-step multi-view kernel based clustering
Authors
Keywords
Multi-view clustering, One-step clustering, Graph learning, Spectral representation, Nonnegative embedding, Automatic weighting, Clustering algorithms
Journal
KNOWLEDGE-BASED SYSTEMS
Volume 241, Issue -, Pages 108250
Publisher
Elsevier BV
Online
2022-01-26
DOI
10.1016/j.knosys.2022.108250
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Dual self-paced multi-view clustering
- (2021) Zongmo Huang et al. NEURAL NETWORKS
- Multi-view spectral clustering via constrained nonnegative embedding
- (2021) S. El Hajjar et al. Information Fusion
- Multi-view clustering via clusterwise weights learning
- (2020) Qianli Zhao et al. KNOWLEDGE-BASED SYSTEMS
- Subspace clustering by simultaneously feature selection and similarity learning
- (2020) Guo Zhong et al. KNOWLEDGE-BASED SYSTEMS
- Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering
- (2020) Zhenwen Ren et al. INFORMATION SCIENCES
- Multi-view cluster analysis with incomplete data to understand treatment effects
- (2019) Guoqing Chao et al. INFORMATION SCIENCES
- Multiple kernel subspace clustering with local structural graph and low-rank consensus kernel learning
- (2019) Zhenwen Ren et al. KNOWLEDGE-BASED SYSTEMS
- Multi-graph fusion for multi-view spectral clustering
- (2019) Zhao Kang et al. KNOWLEDGE-BASED SYSTEMS
- Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding
- (2019) Zhanxuan Hu et al. Information Fusion
- Spectral rotation for deep one-step clustering
- (2019) Xiaofeng Zhu et al. PATTERN RECOGNITION
- Auto-weighted multi-view clustering via deep matrix decomposition
- (2019) Shudong Huang et al. PATTERN RECOGNITION
- Multi-view spectral clustering via sparse graph learning
- (2019) Zhanxuan Hu et al. NEUROCOMPUTING
- Multiple Kernel Clustering With Neighbor-Kernel Subspace Segmentation
- (2019) Sihang Zhou et al. IEEE Transactions on Neural Networks and Learning Systems
- Dual Shared-Specific Multiview Subspace Clustering
- (2019) Tao Zhou et al. IEEE Transactions on Cybernetics
- Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification
- (2018) Feiping Nie et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Multiview Clustering Based on Non-Negative Matrix Factorization and Pairwise Measurements
- (2018) Xiumei Wang et al. IEEE Transactions on Cybernetics
- Accelerated Two-Stage Particle Swarm Optimization for Clustering Not-Well-Separated Data
- (2018) Xiangping Xu et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Non-Negative Matrix Factorization With Dual Constraints for Image Clustering
- (2018) Zuyuan Yang et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Multiview Consensus Graph Clustering
- (2018) Kun Zhan et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Auto-weighted multi-view clustering via kernelized graph learning
- (2018) Shudong Huang et al. PATTERN RECOGNITION
- Kernel-driven similarity learning
- (2017) Zhao Kang et al. NEUROCOMPUTING
- Graph Learning for Multiview Clustering
- (2017) Kun Zhan et al. IEEE Transactions on Cybernetics
- Weighted Multi-view Clustering with Feature Selection
- (2016) Yu-Meng Xu et al. PATTERN RECOGNITION
- Multiview Spectral Embedding
- (2010) Tian Xia et al. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
- Real-Time Computerized Annotation of Pictures
- (2008) Jia Li et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started