Clusterpath An Algorithm for Clustering using Convex Fusion Penalties

Toby Dylan Hocking 1, 2, 3 Armand Joulin 1 Francis Bach 1 Jean-Philippe Vert 2, 3
1 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We present a new clustering algorithm by proposing a convex relaxation of hierarchical clustering, which results in a family of objective functions with a natural geometric interpretation. We give efficient algorithms for calculating the continuous regularization path of solutions, and discuss relative advantages of the parameters. Our method experimentally gives state-of-the-art results similar to spectral clustering for non-convex clusters, and has the added benefit of learning a tree structure from the data.
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Submitted on : Monday, May 9, 2011 - 6:59:24 PM
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  • HAL Id : hal-00591630, version 1


Toby Dylan Hocking, Armand Joulin, Francis Bach, Jean-Philippe Vert. Clusterpath An Algorithm for Clustering using Convex Fusion Penalties. 28th international conference on machine learning, Jun 2011, United States. pp.1. ⟨hal-00591630⟩



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