In most practical applications, it's common to utilize multiple features from different views to represent one object. Among these works, multi-view subspace-based clustering has gained extensive attention from many researchers, which aims to provide clustering solutions to multi-view data. However, most existing methods fail to take full use of the locality geometric structure and similarity relationship among samples under the multi-view scenario. To solve these issues, we propose a novel multi-view learning method with locality relationship constraint to explore the problem of multi-view clustering, called Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF). LRC-MCF aims to explore the diversity, geometric, consensus and complementary information among different views, by capturing the locality relationship information and the common similarity relationships among multiple views. Moreover, LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure and straightforwardly produce the final clusters. To effectually reduce the redundancy of the learned representations, the low-rank constraint on the common similarity matrix is considered additionally. To solve the minimization problem of LRC-MCF, an Alternating Direction Minimization (ADM) method is provided to iteratively calculate all variables LRC-MCF. Extensive experimental results on seven benchmark multi-view datasets validate the effectiveness of the LRC-MCF method.
Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning
Jiangnan University（江南大学）；Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence（江苏省模式识别与计算智能工程实验室）；University of Surrey（萨里大学）
Hefei University of Technology（合肥工业大学）；Soochow University（苏州大学）；University of Queensland（昆士兰大学）；University of Georgia（佐治亚大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；ETH Zurich（苏黎世联邦理工学院）
Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning
Harbin Institute of Technology（哈尔滨工业大学）；National Institute of Informatics（国立信息学研究所）；The University of Electro-Communications（电气通信大学）；Wuhan University（武汉大学）；The University of Tokyo（东京大学）
MSSP: Multi-Scale Spatially-Constrained Partition for Unsupervised Semantic Segmentation of 3D Point Clouds
College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）；School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）
Superpixel Segmentation Based on Spatially Constrained Subspace Clustering
Huazhong University of Science and Technology（华中科技大学）；City University of Hong Kong（香港城市大学）；Southeast University（东南大学）；Beijing Jiaotong University（北京交通大学）
Graph Constrained Data Representation Learning for Human Motion Segmentation
Institut de Robótica i Informática Industrial (CSIC-UPC)（工业机器人研究所（CSIC-UPC））；Univ. of Barcelona（巴塞罗那大学）；CNRS, IRIT, Univ. of Toulouse（法国国家科学研究中心，图卢兹计算机科学研究所，图卢兹大学）
MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classification
School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China, and the Affiliated Tumor Hospital of Xinjiang Medical University, Xinjiang 830011, China（新疆大学计算机科学与技术学院，中国新疆乌鲁木齐830046，以及新疆医科大学附属肿瘤医院，中国新疆830011）；Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY 10027, USA（哥伦比亚大学福基金会工程与应用科学学院，美国纽约纽约州10027）；School of Nursing, The Hong Kong Polytechnic University, Hong Kong 999077, SAR China（香港理工大学护理学院，中国香港999077，香港特别行政区）
LMU Munich（慕尼黑大学）；Fraunhofer Institute for Integrated Circuits（弗劳恩霍夫集成电路研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Georgia Institute of Technology（佐治亚理工学院）
A constrained clustering based approach for matching a collection of feature sets
East China Normal University（华东师范大学）；IBM Research – China（IBM中国研究院）；Shanghai Jiao Tong University（上海交通大学）；Georgia Institute of Technology（佐治亚理工学院）；IBM Watson Research Center（IBM沃森研究中心）
