Essential Tensor Learning for Multi-View Spectral Clustering

DOI10.1109/TIP.2019.2916740WikidataQ92882324 ScholiaQ92882324MaRDI QIDQ5238665

Zhouchen Lin, Hongbin Zha, Jianlong Wu

Publication date: 28 October 2019

Published in: IEEE Transactions on Image Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.03602

Mathematics Subject Classification ID

Information and communication theory, circuits (94-XX)

Related Items (10)

Multi-view spectral clustering via common structure maximization of local and global representations ⋮ Multi-view graph embedding clustering network: joint self-supervision and block diagonal representation ⋮ Learning task-driving affinity matrix for accurate multi-view clustering through tensor subspace learning ⋮ Fine-grained similarity fusion for multi-view spectral clustering ⋮ One-step incomplete multiview clustering with low-rank tensor graph learning ⋮ Low-rank tensor approximation with local structure for multi-view intrinsic subspace clustering ⋮ Multiple graphs learning with a new weighted tensor nuclear norm ⋮ Low-rank tensor constrained co-regularized multi-view spectral clustering ⋮ MulticlusterKDE: a new algorithm for clustering based on multivariate kernel density estimation ⋮ Nonconvex multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix*

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