Structure-Preserving Transformers for Sequences of SPD Matrices

arXiv:2309.07579v5 Announce Type: replace
Abstract: 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 …

abstract analysis arxiv attention attention mechanisms auto beyond context cs.lg data eess.sp images non-euclidean paper positive transformer transformers type types