DATAIA Seminar | « Distance learning using Euclidean percolation: Following Fermat's principle » - Matthieu Jonckheere
In unsupervised statistical learning tasks such as clustering, recommendation, or dimension reduction, a notion of distance or similarity between points is crucial but usually not directly available as an input. We discuss recent techniques to infer a metric from observed data. Then we propose a new density-based estimator for weighted geodesic distances that takes into account the underlying density of the data, and that is suitable for nonuniform data lying on a manifold of lower dimension than the ambient space. The consistency of the estimator is proven using tools from first passage percolation. We then discuss its properties and implementation and evaluate its performance for clustering tasks.
Joint work with P. Groisman and F. Sapienza.
Matthieu Jonckheere received his PhD in applied mathematics from the Ecole Polytechnique (Paris, France). He later completed a postdoc at CWI (Amsterdam) and became an assistant professor at Eindhoven University of Technology. He is now a Conicet researcher and professor at the University of Buenos Aires. He has worked extensively in probability theory and performance evaluation of information and communication systems and more recently in unsupervised learning.
The seminar will take place on November 7 from 2pm to 4pm at CentraleSupélec - Eiffel building.
Registration free but mandatory within the limit of available seats.
For security reasons, no access to the conference room for unregistered participants
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