Fast Approximate Quadratic Programming for Graph Matching

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valu...

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Veröffentlicht in:PloS one Jg. 10; H. 4; S. e0121002
Hauptverfasser: Vogelstein, Joshua T., Conroy, John M., Lyzinski, Vince, Podrazik, Louis J., Kratzer, Steven G., Harley, Eric T., Fishkind, Donniell E., Vogelstein, R. Jacob, Priebe, Carey E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 17.04.2015
Public Library of Science (PLoS)
Schlagworte:
Accuracy
Algorithms
Animals
Applied mathematics
Artificial intelligence
Big Data
Brain
Brain research
Caenorhabditis elegans
Caenorhabditis elegans - physiology
Comparative analysis
Computer vision
Connectome
Convex analysis
Data management
Domains
Efficiency
Graph matching
Graph theory
Graphs
Laboratories
Mental disorders
Nematodes
Nervous system
Neurosciences
Operations research
Optimization techniques
Pattern recognition
Quadratic programming
Traveling salesman problem
United States > US
Baltimore Maryland
ISSN:1932-6203, 1932-6203
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Beschreibung
Zusammenfassung:Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of efficiently and accurately matching the large graphs common in big data, we present our graph matching algorithm, the Fast Approximate Quadratic assignment algorithm. We empirically demonstrate that our algorithm is faster and achieves a lower objective value on over 80% of the QAPLIB benchmark library, compared with the previous state-of-the-art. Applying our algorithm to our motivating example, matching C. elegans connectomes (brain-graphs), we find that it efficiently achieves performance.
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Conceived and designed the experiments: JV JC RJV SK LP CEP DEF EH. Performed the experiments: JC EH DEF JV SK LP. Analyzed the data: CEP JV JC DEF. Contributed reagents/materials/analysis tools: VL JV SK LP DEF EH. Wrote the paper: JV DEF VL.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0121002