Approximate Sampling Method for Locally Linear Embedding

Hyun-Chul Kim, Kyu-Hwan Jung, Jaewook Lee

Abstract: We deal with the nonlinear manifold learning problem to find a low-dimensional structure in high-dimensional data. Based on Gaussian random fields framework, we propose an approximate sampling method for coordinates on the manifolds. Experimentally the mean of samples are shown to be almost equal to the coordinates obtained by locally linear embedding where the generated set of samples of coordinates show interesting variety.