@inproceedings{f8e248571a3b4fb9b1841494b7081a47,
title = "Learning on graph with Laplacian regularization",
abstract = "We consider a general form of transductive learning on graphs with Laplacian regularization, and derive margin-based generalization bounds using appropriate geometric properties of the graph. We use this analysis to obtain a better understanding of the role of normalization of the graph Laplacian matrix as well as the effect of dimension reduction. The results suggest a limitation of the standard degree-based normalization. We propose a remedy from our analysis and demonstrate empirically that the remedy leads to improved classification performance.",
author = "Ando, \{Rie Kubota\} and Tong Zhang",
year = "2007",
doi = "10.7551/mitpress/7503.003.0009",
language = "English (US)",
isbn = "9780262195683",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
pages = "25--32",
booktitle = "Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference",
note = "20th Annual Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",
}