Rank-constrained nonnegative matrix factorization for data representation

DOI10.1016/j.ins.2020.04.017zbMath1459.68187OpenAlexW3016426920MaRDI QIDQ2023226

Yanyan Li

Publication date: 3 May 2021

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

Full work available at URL: https://doi.org/10.1016/j.ins.2020.04.017

zbMATH Keywords

NMFconvergencegraphconnected componentsLaplacian matrixrank constraint

Mathematics Subject Classification ID

Factorization of matrices (15A23) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50) Computational aspects of data analysis and big data (68T09)

Related Items (3)

Deep random walk of unitary invariance for large-scale data representation ⋮ Robust subspace clustering based on automatic weighted multiple kernel learning ⋮ Adaptive graph guided concept factorization on Grassmann manifold

Cites Work

• Unnamed Item
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Learning the parts of objects by non-negative matrix factorization

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