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Statistical Challenges in Modern Astronomy V, State College (PA) : États-Unis (2011)
Discussion on "Exploiting Non-Linear Structure in Astronomical Data for Improved Statistical Inference" by Ann B. Lee and Peter E. Freeman
Didier Fraix-Burnet 1
(2012)

Both dimensionality reduction and classification seek a reduced simpler form of the data. The first one works with the parameter space, while classification works with the object space. Ideally, one wishes to find a parameter space in which the points are naturally gathered into distinct groups and, as a physicist more particularly, data points can fit our model curves. I want to point out that dimensionality reduction methods and classification approaches are highly complementary and should even be carried out together. Astrophysical objects are complex, so that numerical simulations are now a common tools to do physics. Model fitting has thus become a comparison between populations (the observed ones and the synthetic ones) rather than plotting a curve onto data points. This is exactly the role of statistics.
1 :  Institut de Planétologie et d'Astrophysique de Grenoble (IPAG)
CNRS : UMR5274 – INSU – Université Joseph Fourier - Grenoble I – OSUG
Statistiques/Applications

Planète et Univers/Astrophysique/Instrumentation et méthodes pour l'astrophysique

Physique/Astrophysique/Instrumentation et méthodes pour l'astrophysique
Classification – Dimensionality Reduction – Populations
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