Multiple graphs learning with a new weighted tensor nuclear norm

DOI10.1016/J.NEUNET.2020.10.010zbMath1475.68301DBLPjournals/nn/XieGDYG21OpenAlexW3093538588WikidataQ101127668 ScholiaQ101127668MaRDI QIDQ2055061

Yanyan Li

Publication date: 3 December 2021

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2020.10.010

zbMATH Keywords

graph learningmulti-view clusteringweighted tensor nuclear norm

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Norms of matrices, numerical range, applications of functional analysis to matrix theory (15A60)

Related Items (5)

Multi-view graph embedding clustering network: joint self-supervision and block diagonal representation ⋮ Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization ⋮ Online subspace learning and imputation by tensor-ring decomposition ⋮ Fast multiple graphs learning for multi-view clustering ⋮ Imbalanced low-rank tensor completion via latent matrix factorization

Uses Software

• UCI-ml

Cites Work

• Factorization strategies for third-order tensors
• On unifying multi-view self-representations for clustering by tensor multi-rank minimization
• Weighted nuclear norm minimization and its applications to low level vision
• A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
• On the Best Rank-1 and Rank-(R₁ ,R₂ ,. . .,R_N) Approximation of Higher-Order Tensors
• Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus
• Consensus Regularized Multi-View Outlier Detection
• Essential Tensor Learning for Multi-View Spectral Clustering

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