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Vector Space Embedding of Graphs via Statistics of Labelling Information

Vector Space Embedding of Graphs via Statistics of Labelling Information. Ernest Valveny Computer Vision Center Computer Science Departament - UAB. Problem to be solved. How do we solve the problem?. Motivation ( discrete case ). Extension to continuous case.

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Vector Space Embedding of Graphs via Statistics of Labelling Information

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  1. Vector Space Embedding of Graphs viaStatistics of LabellingInformation Ernest Valveny Computer VisionCenter Computer Science Departament - UAB

  2. Problem to be solved

  3. How do we solve the problem?

  4. Motivation (discretecase)

  5. Extension to continuouscase

  6. Extension to continuouscase

  7. Extension to continuouscase (hardversion)

  8. Extension to continuouscase (softversion) • Softassignment of nodes to representatives • Thefrequency of eachword is theaccumulation for all nodes

  9. Someissues • Selection of the set of representatives • K-means • Fuzzy k-means • Spanningprototypes • Mixture of gaussians • Sparsityandhigh-dimensionality of representation • Featureselection • Combination of different sets of representatives • Multipleclassifiersystemswithdifferentnumber of representatives

  10. Someresults (discretecase)

  11. Someresults (continuouscase)

  12. Someresults (continuouscase)

  13. Some results (continuous case)

  14. Conclusions • Simple embeddingmethodology • Computationallyefficient • Based on unaryandbinaryrelationsbetween nodes • Goodresults for classificationandclustering

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