On unifying multi-view self-representations for clustering by tensor multi-rank minimization

DOI10.1007/s11263-018-1086-2zbMath1458.68257arXiv1610.07126OpenAlexW2565835729WikidataQ130025574 ScholiaQ130025574MaRDI QIDQ2193788

Yanyun Qu, Dacheng Tao, Yan Liu, Wensheng Zhang, Lei Zhang, Yuan Xie

Publication date: 20 August 2020

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.07126

zbMATH Keywords

subspace clusteringmulti-view featuresT-VSDtensor multi-rank

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Multilinear algebra, tensor calculus (15A69) Machine vision and scene understanding (68T45)

Related Items (12)

Generalized tensor function via the tensor singular value decomposition based on the T-product ⋮ Continuity, differentiability and semismoothness of generalized tensor functions ⋮ Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint ⋮ Multi-view spectral clustering via common structure maximization of local and global representations ⋮ Tensor subspace clustering using consensus tensor low-rank representation ⋮ Low-rank tensor approximation with local structure for multi-view intrinsic subspace clustering ⋮ Multiview Clustering of Images with Tensor Rank Minimization via Nonconvex Approach ⋮ T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming ⋮ Multiple graphs learning with a new weighted tensor nuclear norm ⋮ Tensorized multi-view subspace representation learning ⋮ Low-rank tensor constrained co-regularized multi-view spectral clustering ⋮ Nonconvex multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix*

Uses Software

• Vlfeat
• MatConvNet
• ImageNet

Cites Work

• Unnamed Item
• Unnamed Item
• Tensor Decompositions and Applications
• Iteration complexity analysis of multi-block ADMM for a family of convex minimization without strong convexity
• Factorization strategies for third-order tensors
• On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
• A Singular Value Thresholding Algorithm for Matrix Completion
• Introduction to Information Retrieval
• Tensor-Based Formulation and Nuclear Norm Regularization for Multienergy Computed Tomography
• Constrained Multi-View Video Face Clustering
• Convex Sparse Spectral Clustering: Single-View to Multi-View
• Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
• Modeling the shape of the scene: A holistic representation of the spatial envelope

This page was built for publication: On unifying multi-view self-representations for clustering by tensor multi-rank minimization