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Persistence-based clustering in Riemannian manifolds (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Chazal, Frédéric Oudot, Steve Skraba, Primoz Guibas, Leonidas J. |
| Abstract | We present a clustering scheme that combines a mode-seeking phase with a cluster merging phase in the corresponding density map. While mode detection is done by a stan-dard graph-based hill-climbing scheme, the novelty of our approach resides in its use of topological persistence to guide the merging of clusters. Our algorithm provides additional feedback in the form of a set of points in the plane, called a persistence diagram (PD), which provably reflects the promi-nences of the modes of the density. In practice, this feed-back enables the user to choose relevant parameter values, so that under mild sampling conditions the algorithm will output the correct number of clusters, a notion that can be made formally sound within persistence theory. The algorithm only requires rough estimates of the density |
| File Format | |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Riemannian Manifold Persistence-based Clustering Persistence Theory Correct Number Stan-dard Graph-based Hill-climbing Scheme Rough Estimate Clustering Scheme Mode-seeking Phase Additional Feedback Persistence Diagram Corresponding Density Map Relevant Parameter Value Mode Detection Topological Persistence |
| Content Type | Text |
| Resource Type | Technical Report |
