Multilevel Spectral Clustering for extreme event characterization
Kelly Grassi, Émilie Poisson Caillault, Alain Lefebvre · OCEANS 2019 - Marseille · 2019
Direct spectral clustering framework was first proposed to extract general pattern events within multivariate time series. This study investigated the way to identify extreme events, i.e. short duration and/or particular events, with no assumption about their emission date, duration and/or shape. A Multilevel Spectral Clustering (M-SC) architecture is proposed and compared with state-of-the-art clustering methods from a simulated manually labeled time series. Due to these promising empirical results, this new deep architecture is applied on marine field data.