Data denoising with self consistency, variance maximization, and the Kantorovich dominance

TL;DR

This paper introduces a novel data denoising framework based on variance maximization and Kantorovich dominance, offering robustness, computational efficiency, and new theoretical insights into distribution comparison.

Contribution

It develops a new denoising approach using self-consistency and variance maximization, introduces Kantorovich dominance as a robust alternative to convex order, and analyzes their theoretical and practical advantages.

Findings

Solutions exist under mild assumptions.

Kantorovich dominance provides enhanced stability.

Numerical examples demonstrate effectiveness of the methods.

Abstract

We introduce a new framework for data denoising, partially inspired by martingale optimal transport. For a given noisy distribution (the data), our approach involves finding the closest distribution to it among all distributions which 1) have a particular prescribed structure (expressed by requiring they lie in a particular domain), and 2) are self-consistent with the data. We show that this amounts to maximizing the variance among measures in the domain which are dominated in convex order by the data. For particular choices of the domain, this problem and a relaxed version of it, in which the self-consistency condition is removed, are intimately related to various classical approaches to denoising. We prove that our general problem has certain desirable features: solutions exist under mild assumptions, have certain robustness properties, and, for very simple domains, coincide with solutions to the relaxed problem. We also introduce a novel relationship between distributions, termed Kantorovich dominance, which retains certain aspects of the convex order while being a weaker, more robust, and easier-to-verify condition. Building on this, we propose and analyze a new denoising problem by substituting the convex order in the previously described framework with Kantorovich dominance. We demonstrate that this revised problem shares some characteristics with the full convex order problem but offers enhanced stability, greater computational efficiency, and, in specific domains, more meaningful solutions. Finally, we present simple numerical examples illustrating solutions for both the full convex order problem and the Kantorovich dominance problem.