NLDR methods for high dimensional NIRS dataset: application to vineyard soils characterization - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

NLDR methods for high dimensional NIRS dataset: application to vineyard soils characterization

Résumé

In the context of vineyard soils characterizationn this paper explores and compare dierent recent Non Linear Dimensionality Reduction (NLDR) methods on a high-dimensional Near InfraRed Spectroscopy (NIRS) dataset. NLDR methods are based on k-neighborhood criterion and Euclidean and fractional distances metrics are tested. Results show that Multiscale Jensen-Shannon Embedding (Ms JSE) coupled with eu-clidean distance outperform all over methods. Application on data is made at global scale and at dierent scale of depth of soil.
Fichier principal
Vignette du fichier
esann2015_Delion.pdf (742.29 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01148868 , version 1 (05-05-2015)

Identifiants

  • HAL Id : hal-01148868 , version 1
  • PRODINRA : 312695

Citer

Clément Delion, Ludovic Journaux, Aurore Payen, Lucile Sautot, Emmanuel Chevigny, et al.. NLDR methods for high dimensional NIRS dataset: application to vineyard soils characterization. 23 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), Michel Verleysen, Apr 2015, Bruges, Belgium. 7 p. ⟨hal-01148868⟩
548 Consultations
245 Téléchargements

Partager

Gmail Facebook X LinkedIn More