Computational methods for the analysis of high-dimensional data
Principal Investigators :
- Dr. Steve Y. Oudot, GEOMETRICA project-team, Inria Saclay Ile de France
- Prof. Leonidas Guibas, Stanford University
- Prof. Yusu Wang, Ohio-State University
- Analysis of shapes via signatures: Analysis and visualization of maps between shapes, and Map-based exploration of intrinsic shape differences and variability.
- Geometric and topological inference in the presence of noise and outliers: Smoothing GPS trajectories using distances to measures, Reconstructing metric graphs from point cloud data, Clustering point cloud data using topological persistence, Homology inference in the presence of unbounded noise and outliers, and Scalar fields analysis in the presence of unbounded noise and outliers.
Publications and Awards:
- 4 Journal articles.
- 3 Conference papers.
- Selected publication below elected among the notable articles of 2013 in computing by ACM and Computing Review: http://computingreviews.com/recommend/bestof/notableitems_2013.cfm
More about COMET: http://geometrica.saclay.inria.fr/collaborations/CoMeT/