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One Step Large-Scale Multi-view subspace clustering Based on Orthogonal Matrix Factorization with Consensus Graph Learning 6th

One Step Large-Scale Multi-view Subspace Clustering Based on...

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6th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Zhang, Xinrui Li, Kai Peng, Jinjia Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Hebei Machine Vis Engn Res Ctr Baoding Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Multi-view clustering has always been a widely concerned issue due to its wide range of applications. Since real-world datasets are usually very large, the clustering problem for large-scale multi-view datasets has always been a research hotspot. Most of the existing methods to solve the problem of large-scale multi-view data usually include several independent steps, namely anchor point generation, graph construction, and clustering result generation, which generate the inflexibility anchor points, and the process of obtaining the cluster indicating matrix and graph constructing are separating from each other, which leads to suboptimal results. Therefore, to address these issues, a one-step multi-view subspace clustering model based on orthogonal matrix factorization with consensus graph learning(CGLMVC) is proposed. Specifically, our method puts anchor point learning, graph construction, and clustering result generation into a unified learning framework, these three processes are learned adaptively to boost each other which can obtain flexible anchor representation and improve the clustering quality. In addition, there is no need for post-processing steps. This method also proposes an alternate optimization algorithm for convergence results, which is proved to have linear time complexity. Experiments on several real world large-scale multi-view datasets demonstrate its efficiency and scalability.

关键词： Multi-view clustering self-expressive subspace clustering Large-scale datasets Orthogonal matrix factorization

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Multi-view subspace clustering with consistent and view-specific latent factors and coefficient matrices

Multi-view subspace clustering with consistent and view-spec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lu, Run-kun Liu, Jian-wei Zuo, Xin Li, Wei-min China Univ Petr Dept Automat Beijing CUP Coll Informat Sci & Engn Beijing Peoples R China Shanghai Univ Sch Comp Engn & Technol Shanghai Peoples R China

ISBN: (纸本)9780738133669

Multi-view learning models the relationships between various observations, and is adept to explore the underlying information of data from multiple perspectives. Since well representation is vital for self-expressive subspace clustering, we propose a method called Multi-View subspace clustering with Consistent and view-Specific Latent Factors and Coefficient Matrices (MVSC-CSLFCM) that explores the consensus and complementary information of multiple views, and we also impose suitable constraints on coefficient matrices corresponding to the obtained view-specific and consistent representations, respectively. Finally, an effective optimization algorithm based on augmented lagrangian multiplier is introduced to optimize our proposed MVSC-CSLFCM. Comprehensive experiments on four real-world data sets demonstrate the superiority of our proposed method by comparing with a series of state-of-art subspace algorithms.

关键词： multi-view learning self-expressive subspace clustering latent representation

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