Robust estimation of boundary using doubly stochastic scaling of Gaussian kernel

This paper addresses the problem of detecting points on or near the boundary of a dataset sampled, potentially with noise, from a compact manifold with boundary. We extend recent advances in doubly stochastic scaling of the Gaussian heat kernel via Sinkhorn iterations to this setting. Our main contributions are: (a) deriving a characterization of the scaling factors for manifolds with boundary, (b) developing a boundary direction estimator, aimed at identifying boundary points, based on doubly stochastic kernel and local principal component analysis, and (c) demonstrating through simulations that the resulting estimates of the boundary points outperform the standard Gaussian kernel-based approach, particularly under noisy conditions.