Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Dominant Sets and Hierarchical Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
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In this paper we develop a new graph representation based on the path-weighted adjacency matrix for characterising global graph structure. The representation is derived from the heat-kernel of the graph. We investigate whether the path-weighted adjacency matrix can be used for the problem of graph partitioning. Here we demonstrate that the method out-performs the use of the adjacency matrix. The main advantage of the new method is that it both preserves partition consistency and shows improved stability to structural error.