Wasserstein distance based semi-supervised manifold learning and application to GNSS multi-path detection

TL;DR

This paper introduces a semi-supervised learning method using Wasserstein distance for graph-based label propagation, demonstrating improved GNSS multi-path interference detection accuracy with limited labeled data.

Contribution

It presents a novel semi-supervised approach leveraging Wasserstein distance for label propagation in deep learning, applied to GNSS multi-path detection.

Findings

Improved classification accuracy over fully supervised methods.

Effective semi-supervised learning with scarce labeled data.

Hyperparameter tuning enhances sensitivity and performance.

Abstract

The main objective of this study is to propose an optimal transport based semi-supervised approach to learn from scarce labelled image data using deep convolutional networks. The principle lies in implicit graph-based transductive semi-supervised learning where the similarity metric between image samples is the Wasserstein distance. This metric is used in the label propagation mechanism during learning. We apply and demonstrate the effectiveness of the method on a GNSS real life application. More specifically, we address the problem of multi-path interference detection. Experiments are conducted under various signal conditions. The results show that for specific choices of hyperparameters controlling the amount of semi-supervision and the level of sensitivity to the metric, the classification accuracy can be significantly improved over the fully supervised training method.