Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Proceedings of the 13th international conference on World Wide Web
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions
The Journal of Machine Learning Research
Semi-supervised protein classification using cluster kernels
Bioinformatics
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Linear prediction models with graph regularization for web-page categorization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast protein classification with multiple networks
Bioinformatics
Selecting features in microarray classification using ROC curves
Pattern Recognition
A survey of kernel and spectral methods for clustering
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised graph clustering: a kernel approach
Machine Learning
Robust label propagation on multiple networks
IEEE Transactions on Neural Networks
Semi-supervised clustering with metric learning: An adaptive kernel method
Pattern Recognition
Aggregation pheromone metaphor for semi-supervised classification
Pattern Recognition
A second order cone programming approach for semi-supervised learning
Pattern Recognition
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We address an issue of semi-supervised learning on multiple graphs, over which informative subgraphs are distributed. One application under this setting can be found in molecular biology, where different types of gene networks are generated depending upon experiments. Here an important problem is to annotate unknown genes by using functionally known genes, which connect to unknown genes in gene networks, in which informative parts vary over networks. We present a powerful, time-efficient approach for this problem by combining soft spectral clustering with label propagation for multiple graphs. We demonstrate the effectiveness and efficiency of our approach using both synthetic and real biological datasets.