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Ecole Nationale de l'Aviation Civile

Academic institutioneurope · fr
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Representative Papers

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

Dec 05, 2025

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.

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Latest Papers

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

Dec 05, 2025

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.

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