Robust Wrapped Gaussian Process Inference for Noisy Angular Data

2025-11-28

AI Summary

Angle data regression requires modeling the inherent circular/spherical manifold structure, yet distributional wrapping under noisy observations couples the latent distribution with the wrapping mechanism, hindering tractable inference. This paper proposes the Monotonic Wrapping Gaussian Process (MW-GP), the first approach to decouple these components by introducing a monotonicity assumption on the wrapping function. MW-GP estimates wrapping locations piecewise in the input space and enables robust Bayesian inference via a Student’s *t* likelihood coupled with elliptical slice sampling. The method significantly improves stability and accuracy in modeling angular responses under noise. In synthetic experiments, it outperforms existing wrapping GP methods. Furthermore, it is successfully applied to phase–distance localization of RFID tags, accurately capturing the unidirectional wrapping relationship between frequency and phase angle.

Technology Category

• Machine Learning: Learning with Manifolds
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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-$2π$ 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.

Problem

• Develops a wrapped Gaussian process for noisy angular data regression.
• Addresses inference challenges in monotonic wrapping scenarios.
• Enhances robustness with Student's t likelihood and efficient sampling.

Innovation

• Monotonic wrapping behavior recognition for accurate inference
• Robust Student's t likelihood and elliptical slice sampling
• Input space partitioning based on estimated wrapping locations