🤖 AI Summary
To address the scarcity of labeled images in GNSS multipath interference detection, this paper proposes a semi-supervised manifold learning method based on the Wasserstein distance. The method leverages optimal transport theory to construct an implicit graph structure, embedding the Wasserstein distance—as a geometrically meaningful similarity metric between samples—into a label propagation mechanism within a deep convolutional neural network framework, enabling robust classification under low-labeling-rate regimes. Compared with fully supervised baselines, the proposed approach achieves significant improvements in classification accuracy across diverse signal conditions, especially when the labeling rate falls below 20%. Its core contribution lies in the first integration of the Wasserstein distance into semi-supervised graph-based learning, effectively capturing both geometric structure and distributional discrepancies in high-dimensional feature spaces. This enhances model sensitivity to sparse annotations and improves generalization capability.
📝 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.