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

Aurore Payen (1) , Ludovic Journaux (2, 1) , Clément Delion (1) , Lucile Sautot (3, 4, 1) , Bruno Faivre (4)

1 AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement
2 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
3 AgroParisTech
4 BGS - Biogéosciences [UMR 6282]

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.

Mots clés: Dimension Reduction; Loire River; Multiscale Jensen-Shannon Embedding; Clustering Algorithms; Nonlinear dimension reduction

Domaines: Traitement du signal et de l'image [eess.SP]

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), Michel Verleysen, Apr 2015, Bruges, Belgium. ⟨hal-01148863⟩