Highdimensional data are implicitly obtained in classification and regression tasks by certain supervised learning algorithms like e.g. Support Vector Machine through Mercer's Theorem. An example where data is explicitly presented in high dimension is given by hyperspectral data. Due to the "curse of dimensionality", dimension-reduction methods become important. Since data are often non-linear, manifold learning techniques are of interest.

The 3-day workshop "Topological Data Analysis and Manifold Learning using Ultrametrics" aims to introduce methods inspired by topology and manifolds for the analysis of high-dimensional data, as well as to practically incorporate some novel ideas from ultrametric analysis in order to obtain faster algorithms. The idea is to test these methods on datasets of interest by the participants and to aim at collaboratively producing novel experimental results, at least in the aftermath of this workshop.

This workshop is brought to you in cooperation of graduate schools KCDS and GRACE. Everyone interested is welcome to join! - For graduate school members, successful participation will be credited with 2 CP.