Efficient unsupervised clustering for spatial bird population analysis along the Loire river

Abstract : This paper focuses on application and comparison of Non Linear Dimensionality Reduction (NLDR) methods on natural high dimensional bird communities dataset along the Loire River (France). In this context, biologists usually use the well-known PCA in order to explain the upstream-downstream gradient.Unfortunately this method was unsuccessful on this kind of nonlinear dataset.The goal of this paper is to compare recent NLDR methods coupled with different data transformations in order to find out the best approach. Results show that Multiscale Jensen-Shannon Embedding (Ms JSE) outperform all over methods in this context.
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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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Contributeur : Ludovic Journaux <>
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Dernière modification le : mercredi 24 mai 2017 - 01:10:43
Document(s) archivé(s) le : lundi 14 septembre 2015 - 19:16:05

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Aurore Payen, Ludovic Journaux, Clément Delion, Lucile Sautot, Bruno Faivre. Efficient unsupervised clustering for spatial bird population analysis along the Loire river. 23 th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN'15), Apr 2015, Bruges, Belgium. 2015. <hal-01148863>

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