Graph Similarity through Entropic Manifold Alignment

DOI10.1137/15M1032454zbMath1452.05111WikidataQ60430808 ScholiaQ60430808MaRDI QIDQ4689636

Miguel A. Lozano, F. Escolano, Edwin R. Hancock

Publication date: 17 October 2018

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

zbMATH Keywords

graph matchinggraph embeddinggraph kernelsgraph similaritynonparametric entropy estimation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Graph algorithms (graph-theoretic aspects) (05C85) Graph representations (geometric and intersection representations, etc.) (05C62) Isomorphism problems in graph theory (reconstruction conjecture, etc.) and homomorphisms (subgraph embedding, etc.) (05C60)

Cites Work

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• Norms and exclusion theorems
• A Riemannian approach to graph embedding
• A class of Rényi information estimators for multidimensional densities
• Image matching using alpha-entropy measures and entropic graphs
• Spectral embedding of graphs.
• Predictive models for min-entropy estimation
• Hitting and commute times in large graphs are often misleading
• An eigendecomposition approach to weighted graph matching problems
• Approximate entropy as a measure of system complexity.
• A distance measure between attributed relational graphs for pattern recognition
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• On Estimation of a Probability Density Function and Mode

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