Exploiting Cluster-Structure to Predict the Labeling of a Graph
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Error bounds of multi-graph regularized semi-supervised classification
Information Sciences: an International Journal
Graph-based analysis of semantic drift in Espresso-like bootstrapping algorithms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Transductive Rademacher complexity and its applications
Journal of Artificial Intelligence Research
Semisupervised multicategory classification with imperfect model
IEEE Transactions on Neural Networks
Semi-supervised learning based on high density region estimation
Neural Networks
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
Information Sciences: an International Journal
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This paper investigates the effect of Laplacian normalization in graph-based semi-supervised learning. To this end, we consider multi-class transductive learning on graphs with Laplacian regularization. Generalization bounds are derived using geometric properties of the graph. Specifically, by introducing a definition of graph cut from learning theory, we obtain generalization bounds that depend on the Laplacian regularizer. We then use this analysis to better understand the role of graph Laplacian matrix normalization. Under assumptions that the cut is small, we derive near-optimal normalization factors by approximately minimizing the generalization bounds. The analysis reveals the limitations of the standard degree-based normalization method in that the resulting normalization factors can vary significantly within each connected component with the same class label, which may cause inferior generalization performance. Our theory also suggests a remedy that does not suffer from this problem. Experiments confirm the superiority of the normalization scheme motivated by learning theory on artificial and real-world data sets.