Carey E. Priebe

Learning Submanifolds for Subsequent Inference on Random Dot Product Graphs, Part 1: Theory

It is shown that, as the auxiliary sample size increases, the risk of the semisupervised rule converges to the risk of an oracle rule that relies on the maximal amount of low-dimensional Euclidean structure that can be extracted from the support manifold.