On non-parametric density estimation on linear and non-linear manifolds using generalized Radon transforms

Abstract

Here we present a new non-parametric approach to density estimation and classification derived from theory in Radon transforms and image reconstruction. We start by constructing a “forward problem” in which the unknown density is mapped to a set of one dimensional empirical distribution functions computed from the raw input data. Interpreting this mapping in terms of Radon-type projections provides an analytical connection between the data and the density with many very useful properties including stable invertibility, fast computation, and significant theoretical grounding. Using results from the literature in geometric inverse problems we give uniqueness results and stability estimates for our methods. We subsequently extend the ideas to address problems in manifold learning and density estimation on manifolds. We introduce two new algorithms which can be readily applied to implement density estimation using Radon transforms in low dimensions or on low dimensional manifolds embedded in ${\mathbb{R}}^d.$ The code for our algorithms can be found here https://github.com/jameswebber1/On-nonparametric-density-estimation-on-linear-and-nonlinear-manifolds. We test our algorithms performance on a range of synthetic 2-D density estimation problems, designed with a mixture of sharp edges and smooth features. We show that our algorithm can offer a consistently competitive performance when compared to the state–of–the–art density estimation methods from the literature.

Keywords:

• Radon transforms
• density estimation
• inverse problems

AMS subject classification:

• 68Q25
• 68R10
• 68U05

Additional information

Funding

This work was funded by Center for Applied Brain and Cognitive Sciences (CABCS) at Tufts University. Shuchin Aeron was supported in part by the NSF CAREER award.