A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Products of Gaussians and probabilistic minor component analysis
Neural Computation
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
A statistical framework for genomic data fusion
Bioinformatics
Linear prediction models with graph regularization for web-page categorization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fast protein classification with multiple networks
Bioinformatics
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Graph based semi-supervised learning with sharper edges
ECML'06 Proceedings of the 17th European conference on Machine Learning
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
An overview of statistical learning theory
IEEE Transactions on Neural Networks
The analysis of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
A generic framework for event detection in various video domains
Proceedings of the international conference on Multimedia
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Bidirectional semi-supervised learning with graphs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation problem on multiple networks and present a new algorithm that automatically integrates structure information brought in by multiple networks. The proposed method is robust in that irrelevant networks are automatically deemphasized, which is an advantage over Tsuda et al.'s approach (2005). We also show that the proposed algorithm can be interpreted as an expectation-maximization (EM) algorithm with a student-t prior. Finally, we demonstrate the usefulness of our method in protein function prediction and digit classification, and show analytically and experimentally that our algorithm is much more efficient than existing algorithms.