Laplace eigenvalues of graphs—a survey
Discrete Mathematics - Algebraic graph theory; a volume dedicated to Gert Sabidussi
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semi-Supervised Learning
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In this paper, we present a generic diffusion kernel for graph-based semi-supervised learning, whose kernel matrix is generated with a Taylor expansion on the generating function of diffusion similarity matrix. The generic diffusion kernel subsumes common known diffusion kernels, and provides a kernel framework for semi-supervised learning. Specifically, we first present the definition of diffusion similarity matrix, and lay the theoretical foundation for our approach. Then we derive the 2- and d-diffusion kernels, and naturally extend them to the generic diffusion kernel. Further we prove that small eigenvalues of the generic diffusion kernel correspond to smooth eigenvectors over the graph. This property is critical for the construction of generic diffusion kernels. Experiments on simulated and benchmark databases demonstrate that the generic diffusion kernel is sound and effective.