Ernest Valveny Computer Vision Center

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Presentation transcript:

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

Extension to continuous case

Extension to continuous case (hard version)

Extension to continuous case (soft version) Soft assignment of nodes to representatives The frequency of each word is the accumulation for all nodes

Some issues Selection of the set of representatives K-means Fuzzy k-means Spanning prototypes Mixture of gaussians Sparsity and high-dimensionality of representation Feature selection Combination of different sets of representatives Multiple classifier systems with different number of representatives

Some results (discrete case)

Some results (continuous case)

Some results (continuous case)

Some results (continuous case)

Conclusions Simple embedding methodology Computationally efficient Based on unary and binary relations between nodes Good results for classification and clustering