Universally consistent vertex classification for latent position graphs
Minh Tang, Daniel L. Sussman, Carey E. Priebe
2013
Abstract
In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate feature maps for latent position graphs with positive definite link function , provided that the latent positions are i.i.d. from some distribution . We then consider the exploitation task of vertex classification where the link function belongs to the class of universal kernels and class labels are observed for a number of vertices tending to infinity and that the remaining vertices are to be classified. We show that minimization of the empirical -risk for some convex surrogate of – loss over a class of linear classifiers with increasing complexities yields a universally consistent classifier, that is, a classification rule with error converging to Bayes optimal for any distribution .
Publication
Annals of Statistics