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Clustering on Manifolds with Dual-Rooted Minimal Spanning Trees

Abstract : In this paper, we introduce a new distance computed from the construction of dual-rooted minimal spanning trees (MSTs). This distance extends Grikschat's approach, exhibits attractive properties and allows to account for both local and global neighborhood information. Furthermore, a function measuring the probability that a point belongs to a detected class is proposed. Some connections with diffusion maps are outlined. The dual-rooted tree-based distance (DRPT) allows us to construct a new affinity matrix for use in a spectral clustering algorithm, or leads to a new data analysis method. Results are presented on benchmark datasets.
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Contributor : Laurent Galluccio <>
Submitted on : Monday, July 19, 2010 - 4:30:01 PM
Last modification on : Wednesday, October 14, 2020 - 1:56:08 PM
Long-term archiving on: : Friday, October 22, 2010 - 4:19:54 PM


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  • HAL Id : hal-00492745, version 2



Laurent Galluccio, Olivier Michel, Pierre Comon. Clustering on Manifolds with Dual-Rooted Minimal Spanning Trees. 16th European Signal Processing Conference EUSIPCO-2010, Aug 2010, Aalborg, Denmark, France. ⟨hal-00492745v2⟩



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