Graph Refinement via Simultaneously Low-Rank and Sparse Approximation

DOI10.1137/21M1446459OpenAlexW4282827530WikidataQ114073971 ScholiaQ114073971MaRDI QIDQ5084519

Li-Min Li, Zhenyue Zhang, Zheng Zhai

Publication date: 24 June 2022

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/21m1446459

zbMATH Keywords

clusteringlow-rank approximationgraph refinementgraph denoisingsparse restriction

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical optimization and variational techniques (65K10) Graph theory (including graph drawing) in computer science (68R10) Numerical linear algebra (65F99) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Uses Software

• GQTPAR
• AS 136

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• GQTPAR
• Statistical mechanics of complex networks
• Computing a Trust Region Step
• The Structure and Function of Complex Networks
• Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Contour and texture analysis for image segmentation