[canceled] Proximity graphs for nonlinear dimension reduction

into algorithms used in applied statistics and pattern recognition.
One prominent example are methods for nonlinear dimension reduction,
which are closely related to the geometric problem of reconstructing
manifolds from a finite set of samples. This talks surveys some
recent developments in both fields, with an emphasis on the problem of
modeling the <I>local</I> structure of points sets using proximity
graphs.