High Order Stochastic Graphlet Embedding for Graph-Based Pattern Recognition

@article{Dutta2017HighOS,
  title={High Order Stochastic Graphlet Embedding for Graph-Based Pattern Recognition},
  author={Anjan Dutta and Hichem Sahbi},
  journal={IEEE transactions on neural networks and learning systems},
  year={2017}
}
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (parts, interest points, and segments) and edges characterize the relationships between these primitives. However, these nonvectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of--explicit/implicit--graph vectorization and… CONTINUE READING
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SHOWING 1-10 OF 47 REFERENCES

Graph kernels

  • S.V.N. Vishwanathan, N. N. Schraudolph, R. Kondor, K. M. Borgwardt
  • JMLR, vol. 11, pp. 1201–1242, .
  • 2010
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