Semi-supervised multi-view clustering based on orthonormality-constrained nonnegative matrix factorization

DOI10.1016/j.ins.2020.05.073zbMath1474.68239OpenAlexW3031222062MaRDI QIDQ2666790

LuYue Lin, Hao Cai, Bo Liu, Yanshan Xiao

Publication date: 23 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.05.073

zbMATH Keywords

NMForthogonal constraintnormalization constraintmulti-view clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Factorization of matrices (15A23) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Nonnegative matrix factorization with local similarity learning ⋮ Fine-grained similarity fusion for multi-view spectral clustering ⋮ Deep matrix factorization with knowledge transfer for lifelong clustering and semi-supervised clustering ⋮ Dual semi-supervised convex nonnegative matrix factorization for data representation ⋮ One-step multi-view spectral clustering with cluster label correlation graph

Cites Work

• Multi-view clustering via multi-manifold regularized non-negative matrix factorization
• Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework

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