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

Abstract : 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.
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Communication dans un congrès
23 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), Apr 2015, Bruges, Belgium. 2015
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Dernière modification le : mercredi 24 mai 2017 - 01:13:17
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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), Apr 2015, Bruges, Belgium. 2015. <hal-01148868>

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