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Author (up) Hana Jarraya; Oriol Ramos Terrades; Josep Llados edit   pdf
url  openurl
  Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
  Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines  
  Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.  
  Address Faro; Portugal; June 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JRL2017a Serial 2953  
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