Andrew Stuart (California Institute of Technology)
Friday, August 24, 2018 - 10:15
SFB 1294 Colloquium, University of Potsdam
Karl-Liebknecht- Str. 24-25, 14476 Potsdam OT Golm, House 27, Room 0.01
Many problems in machine learning require the classification of high dimensional data. One methodology to approach such problems is to construct a graph whose vertices are identified with data points, with edges weighted according to some measure of affinity between the data points. Algorithms such as spectral clustering, probit classification and the Bayesian level set method can all be applied in this setting. The goal of the talk is to describe these algorithms for classification, and analyze them in the limit of large data sets. Doing so leads to interesting problems in the calculus of variations, in stochastic partial differential equations and in Monte Carlo Markov Chain, all of which will be highlighted in the talk. These limiting problems give insight into the structure of the classification problem, and algorithms for it.
submitted by Liv Heinecke (liv.heinecke@uni-potsdam.de, 0331-977-203137)