Wasserstein distance based semi-supervised manifold learning and application to GNSS multi-path detection
This addresses multi-path interference detection in GNSS applications, but it is incremental as it adapts existing semi-supervised and optimal transport techniques to a specific domain.
The study tackled the problem of learning from scarce labeled image data by proposing a semi-supervised approach using Wasserstein distance for similarity in graph-based learning, applied to GNSS multi-path detection, and showed that classification accuracy can be significantly improved over fully supervised methods under specific hyperparameter conditions.
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.