Robust Wrapped Gaussian Process Inference for Noisy Angular Data

TL;DR

This paper introduces a robust wrapped Gaussian process model that effectively handles noisy angular data by recognizing monotonic wrapping behavior, partitioning input space, and employing Student's t likelihood with elliptical slice sampling.

Contribution

The authors propose a novel wrapped GP formulation that accounts for unidirectional wrapping, improving inference accuracy in noisy angular data scenarios.

Findings

Outperforms existing wrapped GP methods on simulated data

Accurately localizes RFID tags using phase-angle modeling

Demonstrates robustness with Student's t likelihood and ESS sampling

Abstract

Angular data are commonly encountered in settings with a directional or orientational component. Regressing an angular response on real-valued features requires intrinsically capturing the circular or spherical manifold the data lie on, or using an appropriate extrinsic transformation. A popular example of the latter is the technique of distributional wrapping, in which functions are "wrapped" around the unit circle via a modulo-$2{\pi}$ transformation. This approach enables flexible, non-linear models like Gaussian processes (GPs) to properly account for circular structure. While straightforward in concept, the need to infer the latent unwrapped distribution along with its wrapping behavior makes inference difficult in noisy response settings, as misspecification of one can severely hinder estimation of the other. However, applications such as radiowave analysis (Shangguan et al., 2015) and biomedical engineering (Kurz and Hanebeck, 2015) encounter radial data where wrapping occurs in only one direction. We therefore propose a novel wrapped GP (WGP) model formulation that recognizes monotonic wrapping behavior for more accurate inference in these situations. This is achieved by estimating the locations where wrapping occurs and partitioning the input space accordingly. We also specify a more robust Student's t response likelihood, and take advantage of an elliptical slice sampling (ESS) algorithm for rejection-free sampling from the latent GP space. We showcase our model's preferable performance on simulated examples compared to existing WGP methodologies. We then apply our method to the problem of localizing radiofrequency identification (RFID) tags, in which we model the relationship between frequency and phase angle to infer how far away an RFID tag is from an antenna.