Graph Kernels from the Jensen-Shannon Divergence

  title={Graph Kernels from the Jensen-Shannon Divergence},
  author={Lu Bai and Edwin R. Hancock},
  journal={Journal of Mathematical Imaging and Vision},
Graph-based representations have been proved powerful in computer vision. The challenge that arises with large amounts of graph data is that of computationally burdensome edit distance computation. Graph kernels can be used to formulate efficient algorithms to deal with high dimensional data, and have been proved an elegant way to overcome this computational bottleneck. In this paper, we investigate whether the Jensen-Shannon divergence can be used as a means of establishing a graph kernel. The… CONTINUE READING
35 Citations
36 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 35 extracted citations


Publications referenced by this paper.
Showing 1-10 of 36 references

Similar Papers

Loading similar papers…