Abstract
We show that extending the Gaussian distribution to the domain of graphs corresponds to truncated Gaussian distributions in Euclidean spaces. Based on this observation, we derive a maximum likelihood method for estimating the parameters of the Gaussian on graphs. In conjunction with a naive Bayes classifier, we applied the proposed approach to image classification.
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Jain, B.J., Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs. In: Jiang, X., Ferrer, M., Torsello, A. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2011. Lecture Notes in Computer Science, vol 6658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20844-7_7
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DOI: https://doi.org/10.1007/978-3-642-20844-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20843-0
Online ISBN: 978-3-642-20844-7
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