Incomplete multiview clustering (IMVC) has gained significant attention for its effectiveness in handling missing sample challenges across various views in real-world multiview clustering applications. Most IMVC approaches tackle this problem by either learning consensus representations from available views or reconstructing missing samples using the underlying manifold structure. However, the reconstruction of learned similarity graph tensor in prior studies only exploits the low-tubal-rank information, neglecting the exploration of inter-view correlations. This paper propose a novel joint tensor and inter-view low-rank Recovery (JTIV-LRR), framing IMVC as a joint optimization problem that integrates incomplete similarity graph learning and tensor representation recovery. By leveraging both intra-view and inter-view low rank information, the method achieves robust estimation of the complete similarity graph tensor through sparse noise removal and low-tubal-rank constraints along different modes. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed approach, achieving significant improvements in clustering accuracy and robustness compared to state-of-the-art methods.
RoHyDR: Robust Hybrid Diffusion Recovery for Incomplete Multimodal Emotion Recognition
School of Computer Science and Engineering（计算机科学与工程学院）；South China University of Technology（华南理工大学）；School of Future Technology（未来技术学院）；University of Oxford（牛津大学）；Pengcheng Laboratory（鹏城实验室）；Department of Computer Science（计算机科学系）
Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models
KITE Research Institute（KITE研究机构）；Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）；College of Engineering and Technology（工程与技术学院）
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
School of Computer, National University of Defense Technology（国防科技大学计算机学院）；Academy of Military Sciences（军事科学院）；Chinese PLA General Hospital（中国人民解放军总医院）
Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise
Department of Statistics, University of California, Davis（加州大学戴维斯分校统计系）；Amazon（亚马逊）；Courant Institute of Mathematical Sciences, New York University, New York, 10012, NY, USA（纽约大学Courant数学科学研究所）
FSRF: Factorization-guided Semantic Recovery for Incomplete Multimodal Sentiment Analysis
School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；School of Advanced Manufacturing Engineering, Hefei University（合肥大学先进制造工程学院）；College of Global Talents, BITZH, Beijing Institute of Technology（北京理工大学珠海学院全球人才学院）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）
SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering
College of Computer and Cyberspace Security, Hebei Normal University（河北师范大学计算机与网络空间安全学院）；Department of Statistics and Data Science, Southern University of Science and Technology（南方科技大学统计与数据科学系）；Key Laboratory of Tibetan Information Processing, Ministry of Education, Qinghai Normal University（青海师范大学藏文信息处理教育部重点实验室）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）
Normalized Iterative Hard Thresholding for Tensor Recovery
Shenzhen Key Laboratory of Advanced Machine Learning and Applications（深圳先进机器学习与应用重点实验室）；School of Mathematical Sciences（数学科学学院）；Shenzhen University（深圳大学）；National Center for Applied Mathematics Shenzhen (NCAMS)（深圳应用数学中心）
Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University（北京交通大学交通数据挖掘与分析重点实验室）；School of Medicine, Zhejiang University（浙江大学医学院）
Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance
College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；University of Technology, Sydney（悉尼科技大学）；Hefei University of Technology（合肥工业大学）
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
School of Computation, Information and Technology, Technical University of Munich, Germany（计算、信息与技术学院，慕尼黑技术大学，德国）；Department of Economics, Virginia Tech, USA（经济系，弗吉尼亚理工学院，美国）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与人工智能研究所）
High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization
Graduate School of Engineering, Saitama Institute of Technology, Japan（埼玉工科大学工学研究科）；Tensor Learning Unit, RIKEN Center for Advanced Intelligence Project (AIP), Japan（日本先进情报项目（AIP）RIKEN智能学习单元）；School of Automation, Guangdong University of Technology, China（广东技术大学自动化学院）；School of Computer Science and Technology, Hangzhou Dianzi University, China（杭州电子科技大学计算机科学与技术学院）
