Finding the best not the most: regularized loss minimization subgraph selection for graph classification

DOI10.1016/j.patcog.2015.05.019zbMath1393.68156OpenAlexW2107793528MaRDI QIDQ1669622

Chengqi Zhang, Shirui Pan, Jia Wu, Guodong Long, Xingquan Zhu

Publication date: 3 September 2018

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10453/37319

zbMATH Keywords

classificationfeature selectionsparse learninggraph classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Data learning from big data

Uses Software

• gSpan

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Manifold elastic net: a unified framework for sparse dimension reduction
• Semi-supervised classification and betweenness computation on large, sparse, directed graphs
• A quantum Jensen-Shannon graph kernel for unattributed graphs
• gBoost: a mathematical programming approach to graph classification and regression
• 10.1162/153244303322753616
• Regularization and Variable Selection Via the Elastic Net
• Linear programming boosting via column generation

This page was built for publication: Finding the best not the most: regularized loss minimization subgraph selection for graph classification