A Unified Framework for Structured Graph Learning via Spectral Constraints

zbMath1498.68246arXiv1904.09792MaRDI QIDQ4969059

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Publication date: 5 October 2020

Full work available at URL: https://arxiv.org/abs/1904.09792

zbMATH Keywords

clusteringadjacency matrixLaplacian matrixMarkov random fieldspectral graph theoryGaussian graphical modelbipartite structurespectral similaritystructured graph learning

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Graphs and linear algebra (matrices, eigenvalues, etc.) (05C50) Probabilistic graphical models (62H22)

Related Items (3)

Multiway \(p\)-spectral graph cuts on Grassmann manifolds ⋮ Block Structured Graph Priors in Gaussian Graphical Models ⋮ spectralGraphTopology

Uses Software

• R
• UCI-ml
• CVX
• Kernlab
• MNIST
• CVXR

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