Distance metric learning for graph structured data

DOI10.1007/s10994-021-06009-3OpenAlexW3003216575WikidataQ114689644 ScholiaQ114689644MaRDI QIDQ2071320

Masayuki Karasuyama, Tomoki Yoshida, Ichiro Takeuchi

Publication date: 28 January 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.00727

zbMATH Keywords

convex optimizationinterpretabilitymetric learninggraph miningstructured data

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Neural predictor-based automated graph classifier framework

Uses Software

• UCI-ml
• LIBSVM
• LIBLINEAR
• gSpan
• PrefixSpan
• LMNN
• graph2vec
• MoTeX
• Sub2vec
• gBoost

Cites Work

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• Unsupervised learning for graph matching
• A survey of graph edit distance
• Automatic learning of cost functions for graph edit distance
• Good edit similarity learning by loss minimization
• gBoost: a mathematical programming approach to graph classification and regression
• Pathwise coordinate optimization
• A new approach to the maximum-flow problem
• Safe Feature Elimination in Sparse Supervised Learning
• Discriminative frequent subgraph mining with optimality guarantees
• Safe Triplet Screening for Distance Metric Learning
• Learning Theory and Kernel Machines
• Regularization and Variable Selection Via the Elastic Net
• Transactions on Computational Systems Biology III
• A simple decomposition method for support vector machines

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