In many real-world applications, we have access to multiple views of the data, each of which characterizes the data from a distinct aspect. Several previous algorithms have demonstrated that one can achieve better clustering accuracy by integrating information from all views appropriately than using only an individual view. Owing to the effectiveness of spectral clustering, many multi-view clustering methods are based on it. Unfortunately, they have limited applicability to large-scale data due to the high computational complexity of spectral clustering. In this work, we propose a novel multi-view spectral clustering method for large-scale data. Our approach is structured under the guided co-training scheme to fuse distinct views, and uses the sampling technique to accelerate spectral clustering. More specifically, we first select $p$ ($\ll n$) landmark points and then approximate the eigen-decomposition accordingly. The augmented view, which is essential to guided co-training process, can then be quickly determined by our method. The proposed algorithm scales linearly with the number of given data. Extensive experiments have been performed and the results support the advantage of our method for handling the large-scale multi-view situation.
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
School of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程学院）；College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）
Faculty of Computer Science, University of Vienna（维也纳大学计算机科学学院）；UniVie Doctoral School Computer Science, University of Vienna（维也纳大学UniVie计算机科学博士学院）；LMU Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Faculty of Philological and Cultural Studies, University of Vienna（维也纳大学语言与文化学院）
Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering
Nanjing University of Posts and Telecommunications（南京邮电大学）；University of Trento（特伦托大学）；Texas State University（得克萨斯州立大学）；Wuhan University（武汉大学）；Minjiang University（闽江学院）
Pose-Guided Self-Training with Two-Stage Clustering for Unsupervised Landmark Discovery
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of Heidelberg（海德堡大学）；Information Technology University of Punjab（旁遮普信息技术大学）
Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；Intelligent Game and Decision Lab（智能游戏与决策实验室）；National University of Singapore（新加坡国立大学）；Chinese PLA General Hospital（中国人民解放军总医院）
Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；UniVie Doctoral School Computer Science, University of Vienna（维也纳大学UniVie计算机博士学院）；Georg-August-Universität Göttingen（哥廷根大学）；Faculty of Philological and Cultural Studies, University of Vienna（维也纳大学语言与文化学院）
Enhancing Multi-Camera People Tracking with Anchor-Guided Clustering and Spatio-Temporal Consistency ID Re-Assignment
University of Washington（华盛顿大学）；Electronics and Telecommunications Research Institute（电子与电信研究所）；National Center for High-Performance Computing（国家高性能计算中心）
提出一种结合锚定引导聚类与时空一致性的多摄像机多人员跟踪方法，在CVPR AI City Challenge 2023上以95.36% IDF1获第一名。
Robust Kernelized Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization
Fujian Key Laboratory of Sensing and Computing（福建省感知与计算重点实验室）；State Key Laboratory of Intelligent Control for Smart City（智能城市智能控制国家重点实验室）；School of Information Science and Management of Complex Systems and Engineering, Xiamen University（厦门大学信息科学与复杂系统与管理工程学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Multi-level Reliable Guidance for Unpaired Multi-view Clustering
School of Mathematical Sciences, Nanjing Normal University（南京师范大学数学学院）；School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）；School of Computing and Information Technology, University of Wollongong（沃林戈大学计算与信息科技学院）
Guided interactive image segmentation using machine learning and color based data set clustering
Leipzig University（莱比锡大学）；IfADo - Leibniz Research Centre for Working Environment and Human Factors（莱布尼茨工作环境与人因研究中心）；INRIA Paris（法国国家信息与自动化研究所巴黎中心）；Sorbonne Université（索邦大学）
Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework
University of Melbourne（墨尔本大学）；Monash University（莫纳什大学）；University of New South Wales（新南威尔士大学）；Northwestern Polytechnical University（西北工业大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Qualcomm Technologies International GmbH（高通技术国际有限公司）；Arriver Software AB（Arriver软件公司）；Linköping University（林雪平大学）；Qualcomm SARL（高通法国公司）；Qualcomm Technologies, Inc（高通技术公司）；KTH Royal Institute of Technology（皇家理工学院（瑞典））
Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data
Guangxi Normal University（广西师范学院）；University of Electronic Science and Technology of China（电子科技大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Hainan University（海南大学）
Consistent Multiple Graph Embedding for Multi-View Clustering
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京交通大学先进信息科学与网络技术北京重点实验室）
Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints
College of Information Science and Technology, Hainan University（海南大学信息科学技术学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Hainan University（海南大学）
