Papers › Structure-Preserving Transformers for Sequences of SPD Matrices
Structure-Preserving Transformers for Sequences of SPD Matrices
Mathieu Seraphim, Alexis Lechervy, Florian Yger, Luc Brun, Olivier Etard
In recent years, Transformer-based auto-attention mechanisms have been successfully applied to the analysis of a variety of context-reliant data types, from texts to images and beyond, including data from non-Euclidean geometries. In this paper, we present such a mechanism, designed to classify sequences of Symmetric Positive Definite matrices while preserving their Riemannian geometry throughout the analysis. We apply our method to automatic sleep staging on timeseries of EEG-derived covariance matrices from a standard dataset, obtaining high levels of stage-wise performance.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sleep Stage Detection | MASS SS3 | SPDTransNet | Macro-F1 | 0.8124 | #6 of 6 | Archive leaderboard | report |
| Sleep Stage Detection | MASS SS3 | SPDTransNet | Macro-averaged Accuracy | 84.40% | #6 of 6 | Archive leaderboard | report |
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