SuSE : Subspace Selection embedded in an EM algorithm - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2006

SuSE : Subspace Selection embedded in an EM algorithm


Subspace clustering is an extension of traditional clustering that seeks to find clusters embedded in different subspaces within a dataset. This is a particularly important challenge with high dimensional data where the curse of dimensionality occurs. It also has the benefit of providing smaller descriptions of the clusters found. In this field, we show that using probabilistic models provides many advantages over other existing methods. In particular, we show that the difficult problem of the parameter settings of subspace clustering algorithms can be seen as a model selection problem in the framework of probabilistic models. It thus allows us to design a method that does not require any input parameter from the user. We also point out the interest in allowing the clusters to overlap. And finally, we show that it is well suited for detecting the noise that may exist in the data, and that this helps to provide a more understandable representation of the clusters found.
Fichier principal
Vignette du fichier
CAP06SUSE.pdf (206.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00471311 , version 1 (07-04-2010)


  • HAL Id : inria-00471311 , version 1


Laurent Candillier, Isabelle Tellier, Fabien Torre, Olivier Bousquet. SuSE : Subspace Selection embedded in an EM algorithm. Conférence d'Apprentissage, 2006, Trégastel, France. ⟨inria-00471311⟩
82 View
61 Download


Gmail Facebook Twitter LinkedIn More