Low-rank tensor constrained co-regularized multi-view spectral clustering

DOI10.1016/J.NEUNET.2020.08.019zbMath1475.68304OpenAlexW3083629845WikidataQ99413652 ScholiaQ99413652MaRDI QIDQ2057765

Yanyan Li

Publication date: 7 December 2021

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2020.08.019

zbMATH Keywords

spectral clusteringweighted tensor nuclear normmulti-views learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Multilinear algebra, tensor calculus (15A69)

Related Items (3)

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 ⋮ Online subspace learning and imputation by tensor-ring decomposition

Uses Software

• NUS-WIDE

Cites Work

• Reduced rank regression via adaptive nuclear norm penalization
• Factorization strategies for third-order tensors
• A trace inequality of John von Neumann
• Orthogonal self-guided similarity preserving projection for classification and clustering
• On unifying multi-view self-representations for clustering by tensor multi-rank minimization
• Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus
• Essential Tensor Learning for Multi-View Spectral Clustering
• Modeling the shape of the scene: A holistic representation of the spatial envelope

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