Multilevel Spectral Clustering for extreme event characterization

被引：1
|
作者：
Grassi, Kelly [1 ,2 ,3 ]
Poisson Caillault, Emilie [2 ]
Lefebvre, Alain [3 ]
机构：
[1] WeatherForce, F-31000 Toulouse, France
[2] ULCO LISIC, F-62228 Calais, France
[3] IFREMER, LER BL, F-62321 Boulogne Sur Mer, France
来源：
OCEANS 2019 - MARSEILLE | 2019年
基金：
欧盟地平线“2020”;
关键词：
Spectral clustering; multilevel systems; extreme events; time series; environmental monitoring;
D O I：
10.1109/oceanse.2019.8867261
中图分类号：
U6 [水路运输]; P75 [海洋工程];
学科分类号：
0814 ; 081505 ; 0824 ; 082401 ;
摘要：
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.
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收藏
页数：7
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