Hypoelliptic Diffusion Maps I: Tangent Bundles
This work addresses the problem of enhancing dimensionality reduction for researchers in manifold learning by incorporating additional geometric structures, though it appears incremental as it builds on existing methods like Vector Diffusion Maps.
The paper introduces Hypoelliptic Diffusion Maps (HDM), a framework that generalizes Diffusion Maps for manifold learning by augmenting data with attached structures like tangent bundles to study local geometry and relationships, revealing connections to sub-Riemannian geometry and hypoelliptic operators.
We introduce the concept of Hypoelliptic Diffusion Maps (HDM), a framework generalizing Diffusion Maps in the context of manifold learning and dimensionality reduction. Standard non-linear dimensionality reduction methods (e.g., LLE, ISOMAP, Laplacian Eigenmaps, Diffusion Maps) focus on mining massive data sets using weighted affinity graphs; Orientable Diffusion Maps and Vector Diffusion Maps enrich these graphs by attaching to each node also some local geometry. HDM likewise considers a scenario where each node possesses additional structure, which is now itself of interest to investigate. Virtually, HDM augments the original data set with attached structures, and provides tools for studying and organizing the augmented ensemble. The goal is to obtain information on individual structures attached to the nodes and on the relationship between structures attached to nearby nodes, so as to study the underlying manifold from which the nodes are sampled. In this paper, we analyze HDM on tangent bundles, revealing its intimate connection with sub-Riemannian geometry and a family of hypoelliptic differential operators. In a later paper, we shall consider more general fibre bundles.