Clusterpath An Algorithm for Clustering using Convex Fusion PenaltiesReport as inadecuate

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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 2 CBIO - Centre de Bioinformatique 3 Cancer et génôme: Bioinformatique, biostatistiques et épidémiologie d-un système complexe

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.

Keywords : Clustering Sparsity Fusion Hierarchical Convex Optimization Unsupervised Learning

Author: Toby Dylan Hocking - Armand Joulin - Francis Bach - Jean-Philippe Vert -



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