When Locally Linear Embedding Hits Boundary

Based on the Riemannian manifold model, we study the asymptotic behavior of a widely applied unsupervised learning algorithm, locally linear embedding (LLE), when the point cloud is sampled from a compact, smooth manifold with boundary. We show several peculiar behaviors of LLE near the boundary that are different from those diffusion-based algorithms. Particularly, LLE converges to a mixed-type differential operator with degeneracy. This study leads to an alternative boundary detection algorithm and two potential approaches to recover the Dirichlet Laplace-Beltrami operator.