3351 20180111115047.0 CTALK-2018-0009 Delouille, Véronique Supervised and non-supervised classification in solar physics using advanced techniques 2017 2017-09-26 Advances in statistical signal processing allow us to gaze at solar data from a new perspective, and to make better predictions. For example, the problem of clustering active regions and sunspots from magnetogram and continuum data can be looked at with image patch analysis and matrix factorization lenses. Such method provides a characterization of fine scale structures encoded e.g. by localized gradients, or locally smooth areas. The resulting clusterings are related to large scale descriptors of an active region such as its size, local magnetic field distribution, and complexity as measured by Mount Wilson classification. Supervised classification in the presence of an imbalanced dataset is another example where recent advances bring added accuracy. I will illustrate this on the problem of separating filaments (FL) from coronal holes (CH) using a labelled dataset of features, where the FL/CH proportion in the observed sample is 6/94. Various strategies for dealing with imbalance will be discussed. This is a generic problem, that may also appear e.g. when one want to distinguish flare-productive from more quiet active regions. NO Moon, Kevin Hero, Alfred O. Hofmeister, Stefan Reiss, Martin Veronig, Astrid Space Weather: a Multi-Disciplinary Approach, Lorentz Center workshop, Leiden, NL veronique.delouille@observatoire.be Invited CTALKINVI