How far are two symmetric matrices from commuting? With an application to object characterisation and identification in metal detection

TL;DR

This paper introduces novel semi-metrics to measure the distance between symmetric matrices, facilitating object characterization in metal detection and improving machine learning classification robustness.

Contribution

The work develops eigenvector-independent semi-metrics that approximate Riemannian metrics for small angles, enhancing analysis of symmetric matrices in noisy measurement scenarios.

Findings

New semi-metrics effectively approximate Riemannian distances.

Application to metal detection improves object identification.

Bayesian classifiers demonstrate successful classification results.

Abstract

Examining the extent to which measurements of rotation matrices are close to each other is challenging due measurement noise. To overcome this, data is typically smoothed and Riemannian and Euclidean metrics are applied. However, if rotation matrices are not directly measured and are instead formed by eigenvectors of measured symmetric matrices, this can be problematic if the associated eigenvalues are close. In this work, we propose novel semi-metrics that can be used to approximate the Riemannian metric for small angles. Our new results do not require eigenvector information and are beneficial for measured datasets. There are also issues when using comparing rotational data arising from computational simulations and it is important that the impact of the approximations on the computed outputs is properly assessed to ensure that the approximations made and the finite precision arithmetic are not unduly polluting the results. In this work, we examine data arising from object characterisation in metal detection using the complex symmetric rank two magnetic polarizability tensor (MPT) description, we rigorously analyse the effects of our numerical approximations and apply our new approximate measures of distance to the commutator of the real and imaginary parts of the MPT to this application. Our new approximate measures of distance provide additional feature information, which is invariant of the object orientation, to aid with object identification using machine learning classifiers. We present Bayesian classification examples to demonstrate the success of our approach.