@inproceedings{aaf671ce8f3e4abaa63958f09b447c92,
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",
note = "Publisher Copyright: {\textcopyright} NIPS 2006.All rights reserved; 19th International Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",
year = "2006",
language = "English (US)",
series = "NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "25--32",
editor = "Bernhard Scholkopf and Platt, {John C.} and Thomas Hofmann",
booktitle = "NIPS 2006",
address = "United States",
}