Optimal detection of the feature matching map in presence of noise and outliers

TL;DR

This paper addresses the challenge of detecting feature matching maps in high-dimensional noisy data with outliers, establishing optimal detection rates and validating them through theoretical analysis and experiments.

Contribution

It introduces a novel detection method based on minimizing the sum of logarithms of distances, with proven optimality and practical validation.

Findings

Detection rates depend on inlier-inlier and inlier-outlier distances, scaling as d^{1/4} and d^{1/2} respectively.

Proposed estimator achieves these rates and is shown to be optimal through lower bounds.

Numerical experiments confirm theoretical predictions on synthetic and real data.

Abstract

We consider the problem of finding the matching map between two sets of $d$ dimensional vectors from noisy observations, where the second set contains outliers. The matching map is then an injection, which can be consistently estimated only if the vectors of the second set are well separated. The main result shows that, in the high-dimensional setting, a detection region of unknown injection can be characterized by the sets of vectors for which the inlier-inlier distance is of order at least $d^{1/4}$ and the inlier-outlier distance is of order at least $d^{1/2}$. These rates are achieved using the estimated matching minimizing the sum of logarithms of distances between matched pairs of points. We also prove lower bounds establishing optimality of these rates. Finally, we report results of numerical experiments on both synthetic and real world data that illustrate our theoretical results and provide further insight into the properties of the estimators studied in this work.