Unsupervised classification: exploring the high dimension
Intervenant : Didier Fraix-Burnet
Dealing with large amount of data is a new problematic task in astrophysics. One may distinguish the management of these data (astroinformatics) and their scientific use (astrostatistics) even if the border is rather fuzzy. Dimensionality reduction in both the number of observations and the number of parameters (observables) is necessary for an easier physical understanding. This is the purpose of classification which has been traditionally eye-based and essentially still is but becomes not possible anymore. In this talk, I present a general overview of machine learning approaches for unsupervised classification, with applications to stars (chemical abundances) and galaxies (spectra).