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
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Convex Optimization
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
The Journal of Machine Learning Research
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Semi-Supervised Learning
Video annotation by graph-based learning with neighborhood similarity
Proceedings of the 15th international conference on Multimedia
A Modeling Approach Using Multiple Graphs for Semi-Supervised Learning
DS '08 Proceedings of the 11th International Conference on Discovery Science
Semi-supervised Learning with Ensemble Learning and Graph Sharpening
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Robust label propagation on multiple networks
IEEE Transactions on Neural Networks
A multilevel approach for learning from labeled and unlabeled data on graphs
Pattern Recognition
Expert Systems with Applications: An International Journal
Directed graph learning via high-order co-linkage analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Supervised neighborhood graph construction for semi-supervised classification
Pattern Recognition
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In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data points' (often symmetric) relationships in input space, without considering directionality. However, relationships may be more informative in one direction (e.g. from labelled to unlabelled) than in the reverse direction, and some relationships (e.g. strong weights between oppositely labelled points) are unhelpful in either direction. Undesirable edges may reduce the amount of influence an informative point can propagate to its neighbours – the point and its outgoing edges have been “blunted.” We present an approach to “sharpening” in which weights are adjusted to meet an optimization criterion wherever they are directed towards labelled points. This principle can be applied to a wide variety of algorithms. In the current paper, we present one ad hoc solution satisfying the principle, in order to show that it can improve performance on a number of publicly available benchmark data sets.