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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.07579 |
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| _version_ | 1866929361236525056 |
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| author | Seraphim, Mathieu Lechervy, Alexis Yger, Florian Brun, Luc Etard, Olivier |
| author_facet | Seraphim, Mathieu Lechervy, Alexis Yger, Florian Brun, Luc Etard, Olivier |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_07579 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Structure-Preserving Transformers for Sequences of SPD Matrices Seraphim, Mathieu Lechervy, Alexis Yger, Florian Brun, Luc Etard, Olivier Machine Learning Signal Processing 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. |
| title | Structure-Preserving Transformers for Sequences of SPD Matrices |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2309.07579 |