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Topology Learning: New Challenges At the Crossing of Machine Learning,
Michael Aupetit · Frederic Chazal · Gilles Gasso · David Cohen-Steiner · pierre gaillard

Fri Dec 07 07:30 AM -- 06:30 PM (PST) @ Westin: Glacier
Event URL: http://topolearnnips2007.insa-rouen.fr/ »

There is a growing interest in Machine Learning, in applying geometrical and topological tools to high-dimensional data analysis and processing. Considering a finite set of points in a high-dimensional space, the approaches developed in the field of Topology Learning intend to learn, explore and exploit the topology of the shapes (topological invariants such as the intrinsic dimension or the Betti numbers), manifolds or not, from which these points are supposed to be drawn. Applications likely to benefit from these topological characteristics have been identified in the field of Exploratory Data Analysis, Pattern Recognition, Process Control, Semi-Supervised Learning, Manifold Learning and Clustering. However it appears that the integration in the Machine Learning and Statistics frameworks of the problems we are faced with in Topology Learning, is still in its infancy. So we wish this workshop to ignite cross-fertilization between Machine Learning, Computational Geometry and Topology, likely to benefit to all of them by leading to new approaches, deeper understanding, and stronger theoretical results of the problems carried by Topology Learning.

Please check the Workshop website for schedule changes.

Author Information

Michael Aupetit (Commissariat à l'Energie Atomique)
Frederic Chazal (INRIA)
Gilles Gasso (LITIS - INSA Rouen Normandie)
David Cohen-Steiner (INRIA Sophia-Antipolis Mediterranee)
pierre gaillard (French Atomic Energy Commission)

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