Unsupervised Learning of High Dimensional Environmental Data Using Local Fractality Concept

Auteurs

Kanevski M., Laib M.

Référence

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12666 LNCS, pp. 130-138, 2021

Description

The research deals with an exploration of high dimensional environmental data using unsupervised learning algorithms and the concept of local fractality. The proposed methodology is applied to geospatial data used for the wind speed prediction in a complex mountainous region. It is shown, that the approach provides important additional information on data manifold useful in data analysis, data visualisation and predictive modelling.

Lien

doi:10.1007/978-3-030-68780-9_13