Robust Vertex Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Li ChenCarey E Priebe

Abstract

For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension. In this paper, we propose a sparse representation vertex classifier which does not require information about the model dimension. This classifier represents a test vertex as a sparse combination of the vertices in the training set and uses the recovered coefficients to classify the test vertex. We prove consistency of our proposed classifier for stochastic blockmodels, and demonstrate that the sparse representation classifier can predict vertex labels with higher accuracy than adjacency spectral embedding approaches via both simulation studies and real data experiments. Our results demonstrate the robustness and effectiveness of our proposed vertex classifier when the model dimension is unknown.

References

Apr 1, 2006·Proceedings of the National Academy of Sciences of the United States of America·David L Donoho, Michael Elad
Oct 10, 2006·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·M E J Newman
Dec 27, 2008·IEEE Transactions on Pattern Analysis and Machine Intelligence·John WrightYi Ma
Feb 10, 2011·PLoS Computational Biology·Lav R VarshneyDmitri B Chklovskii
Sep 21, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Ehsan Elhamifar, René Vidal
Nov 16, 2013·IEEE Transactions on Pattern Analysis and Machine Intelligence·Daniel L SussmanCarey E Priebe
Oct 3, 2014·Proceedings of the National Academy of Sciences of the United States of America·Sofia C Olhede, Patrick J Wolfe

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