An adaptive robust regression method: Application to galaxy spectrum baseline estimation
| Autor: | Olivier Michel, Raphael Bacher, Florent Chatelain |
|---|---|
| Přispěvatelé: | GIPSA - Communication Information and Complex Systems (GIPSA-CICS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre de Recherche Astrophysique de Lyon (CRAL), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS), GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique (GIPSA-SAIGA), Département Automatique (GIPSA-DA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Département Images et Signal (GIPSA-DIS), ERC-MUSICOS, European Project: 339659,EC:FP7:ERC,ERC-2013-ADG,MUSICOS(2014), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Mathematical optimization
Spectral signature Baseline Estimation Astronomy 010401 analytical chemistry Hyperspectral imaging Local regression Least trimmed squares Robust Regression 02 engineering and technology Least Trimmed Squares 01 natural sciences 0104 chemical sciences Robust regression Outlier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Baseline (configuration management) Algorithm [STAT.ME]Statistics [stat]/Methodology [stat.ME] Smoothing Spectroscopy Mathematics |
| Zdroj: | ICASSP 2016-Proceedings ICASSP 2016-41st IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP 2016-41st IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2016, Shanghai, China. pp.4423-4427, ⟨10.1109/ICASSP.2016.7472513⟩ ICASSP |
| Popis: | International audience; In this paper, a new robust regression method based on the Least Trimmed Squares (LTS) is proposed. The novelty of this approach consists in a simple adaptive estimation of the number of outliers. This method can be applied to baseline estimation, for example to improve the detection of gas spectral signature in astronomical hy-perspectral data such as those produced by the new Multi Unit Spec-troscopic Explorer (MUSE) instrument. To do so a method following the general idea of the LOWESS algorithm, a classical robust smoothing method, is developed. It consists in a windowed local linear regression, the local regression being done here by the new adap-tive LTS approach. The developed method is compared with state-of-the art baseline estimated algorithms on simulated data closed to the real data produced by the MUSE instrument. |
| Databáze: | OpenAIRE |
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