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Learning the Graph Edit Costs Based on a Learning Model Applied to Sub-optimal Graph Matching

  • Datos identificativos

    Identificador: imarina:6285542
    Autores:
    Santacruz, PepSerratosa, Francesc
    Resumen:
    Attributed graphs are used to represent patterns composed of several parts in pattern recognition. The nature of these patterns can be diverse, from images, to handwritten characters, maps or fingerprints. Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. It is based on transforming one graph into another through some edit operations such as substitution, deletion and insertion of nodes and edges. It has two main constraints: it requires an adequate definition of the costs of these operations and its computation cost is exponential with regard to the number of nodes. In this paper, we first present a general framework to automatically learn these edit costs considering graph edit distance is computed in a sub-optima way. Then, we specify this framework in two different models based on neural networks and probability density functions. An exhaustive practical validation on 14 public databases, which have different features such as the size of the graphs, the number of attributes or the number of graphs per class have been performed. This validation shows that with the learned edit costs, the accuracy is higher than with some manually imposed costs or other costs automatically learned by previous methods.
  • Otros:

    Autor según el artículo: Santacruz, Pep; Serratosa, Francesc
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Serratosa Casanelles, Francesc d'Assís
    Palabras clave: Probability density function Neural network Learning the graph edit costs Graph embedding into vector Graph edit distance
    Resumen: Attributed graphs are used to represent patterns composed of several parts in pattern recognition. The nature of these patterns can be diverse, from images, to handwritten characters, maps or fingerprints. Graph edit distance has become an important tool in structural pattern recognition since it allows us to measure the dissimilarity of attributed graphs. It is based on transforming one graph into another through some edit operations such as substitution, deletion and insertion of nodes and edges. It has two main constraints: it requires an adequate definition of the costs of these operations and its computation cost is exponential with regard to the number of nodes. In this paper, we first present a general framework to automatically learn these edit costs considering graph edit distance is computed in a sub-optima way. Then, we specify this framework in two different models based on neural networks and probability density functions. An exhaustive practical validation on 14 public databases, which have different features such as the size of the graphs, the number of attributes or the number of graphs per class have been performed. This validation shows that with the learned edit costs, the accuracy is higher than with some manually imposed costs or other costs automatically learned by previous methods.
    Áreas temáticas: Software Neurosciences Neuroscience (miscellaneous) Neuroscience (all) Interdisciplinar General neuroscience Engenharias iv Computer science, artificial intelligence Computer networks and communications Ciência da computação Artificial intelligence
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1370-4621
    Direcció de correo del autor: francesc.serratosa@urv.cat
    Identificador del autor: 0000-0001-6112-5913
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://link.springer.com/article/10.1007%2Fs11063-019-10121-w
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Neural Processing Letters. 51 (1): 881-904
    Referencia de l'ítem segons les normes APA: Santacruz, Pep; Serratosa, Francesc (2020). Learning the Graph Edit Costs Based on a Learning Model Applied to Sub-optimal Graph Matching. Neural Processing Letters, 51(1), 881-904. DOI: 10.1007/s11063-019-10121-w
    DOI del artículo: 10.1007/s11063-019-10121-w
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Artificial Intelligence,Computer Networks and Communications,Computer Science, Artificial Intelligence,Neuroscience (Miscellaneous),Neurosciences,Software
    Probability density function
    Neural network
    Learning the graph edit costs
    Graph embedding into vector
    Graph edit distance
    Software
    Neurosciences
    Neuroscience (miscellaneous)
    Neuroscience (all)
    Interdisciplinar
    General neuroscience
    Engenharias iv
    Computer science, artificial intelligence
    Computer networks and communications
    Ciência da computação
    Artificial intelligence
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