Centre de documentation
scientifique

A fast classification scheme in Raman spectroscopy for the identification of mineral mixtures using a large database with correlated predictors / Corey J. Cochrane in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 8 (August 2015) . - pp 4259 - 4274 Titre : A fast classification scheme in Raman spectroscopy for the identification of mineral mixtures using a large database with correlated predictors Type de document : Article/Communication Auteurs : Corey J. Cochrane, Auteur ; Jordana Blacksberg, Auteur Année de publication : 2015 Article en page(s) : pp 4259 - 4274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rayonnement électromagnétique [Termes IGN] classification automatique [Termes IGN] diffusion de Raman [Termes IGN] régression [Termes IGN] spectroscopie Résumé : (Auteur) Robust classification methods are vital to the successful implementation of many material characterization techniques, particularly where large databases exist. In this paper, we demonstrate an extremely fast classification method for the identification of mineral mixtures in Raman spectroscopy using the large RRUFF database. However, this method is equally applicable to other techniques meeting the large database criteria, these including laser-induced breakdown, X-ray diffraction, and mass spectroscopy methods. Classification of these multivariate datasets can be challenging due in part to the various obscuring features inherently present within the underlying dataset and in part to the volume and variety of information known a priori. Some of the more specific challenges include the observation of mixtures with overlapping spectral features, the use of large databases (i.e., the number of predictors far outweighs the number of observations), the use of databases that contain groups of correlated spectra, and the ever present, clouding contaminants of noise, undesired background, and spectrometer artifacts. Although many existing classification algorithms attempt to address these problems individually, not many address them as a whole. Here, we apply a multistage approach, which leverages well-established constrained regression techniques, to overcome these challenges. Our modifications to conventional algorithm implementations are shown to increase speed and performance of the classification process. Unlike many other techniques, our method is able to rapidly classify mixtures while simultaneously preserving sparsity. It is easily implemented, has very few tuning parameters, does not require extensive parameter training, and does not require data dimensionality reduction prior to classification. Numéro de notice : A2015-386 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2394377 En ligne : https://doi.org/10.1109/TGRS.2015.2394377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76864 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015081	RAB	Revue	Centre de documentation	En réserve L003	Disponible