Heuristic Framework for Multiscale Testing of the Multi-Manifold Hypothesis

Abstract

When analyzing empirical data, we often find that global linear models overestimate the number of parameters required. In such cases, we may ask whether the data lies on or near a manifold or a set of manifolds, referred to as multi-manifold, of lower dimension than the ambient space. This question can be phrased as a (multi-)manifold hypothesis. The identification of such intrinsic multiscale features is a cornerstone of data analysis and representation, and has given rise to a large body of work on manifold learning. In this work, we review key results on multiscale data analysis and intrinsic dimension followed by the introduction of a heuristic, multiscale, framework for testing the multi-manifold hypothesis. Our method implements a hypothesis test on a set of spline-interpolated manifolds constructed from variance-based intrinsic dimensions. The workflow is suitable for empirical data analysis as we demonstrate on two use cases.