Fast manifold landmarking using locality-sensitive hashing

Abstract

© Springer International Publishing AG, part of Springer Nature 2018. Manifold landmarks can approximately represent the low-dimensional nonlinear manifold structure embedded in high-dimensional ambient feature space. Due to the quadratic complexity of many learning algorithms in the number of training samples, selecting a sample subset as manifold landmarks has become an important issue for scalable learning. Unfortunately, state-of-the-art Gaussian process methods for selecting manifold landmarks themselves are not scalable to large datasets. In an attempt to speed up learning manifold landmarks, uniformly selected minibatch stochastic gradient descent is used by the state-of-the-art appro..