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Marcus M. Noack

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#machine learning Preprint Sep 2026

A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

The Sparse Landmark Embedding (SLE) kernel is proposed, and it is demonstrated, using geodesic and Wasserstein distances, that the SLE kernel matches or substantially exceeds domain-specific baselines in both predictive accuracy and uncertainty quantification.

Marcus M. Noack, Maher B. Alghalayini, Mark Risser · 0 citations