Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Kernel Principal Component Analysis
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The Journal of Machine Learning Research
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
A Riemannian approach to graph embedding
Pattern Recognition
Graph embedding using tree edit-union
Pattern Recognition
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian optimization of the scale saliency filter
Image and Vision Computing
Graph Characteristics from the Ihara Zeta Function
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph matching using the interference of continuous-time quantum walks
Pattern Recognition
Graph matching using the interference of discrete-time quantum walks
Image and Vision Computing
Graph characteristics from the heat kernel trace
Pattern Recognition
Nonextensive Information Theoretic Kernels on Measures
The Journal of Machine Learning Research
Quantum walks, Ihara zeta functions and cospectrality in regular graphs
Quantum Information Processing
Characterizing graphs using approximate von Neumann entropy
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Quantifying Complexity in Networks: The von Neumann Entropy
International Journal of Agent Technologies and Systems
Depth-based complexity traces of graphs
Pattern Recognition
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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 Jensen-Shannon kernel is nonextensive information theoretic kernel, and is defined using the entropy and mutual information computed from probability distributions over the structures being compared. To establish a Jensen-Shannon graph kernel, we explore two different approaches. The first of these is based on the von Neumann entropy associated with a graph. The second approach uses the Shannon entropy associated with the probability state vector for a steady state random walk on a graph. We compare the two resulting graph kernels for the problem of graph clustering. We use kernel principle components analysis (kPCA) to embed graphs into a feature space. Experimental results reveal that the method gives good classification results on graphs extracted both from an object recognition database and from an application in bioinformation.