A new family of 12 probabilistic models, introduced recently, aims to simultaneously clus- ter and visualize high-dimensional data. It is based on a mixture model which fits the data into a latent discriminative subspace with an intrinsic dimension bounded by the number of clusters. An estimation procedure, named the Fisher-EM algorithm has also been pro- posed and turns out to outperform other subspace clustering in most situations. Moreover the convergence properties of the Fisher-EM algorithm are discussed; in particular it is proved that the algorithm is a GEM algorithm and converges under weak conditions in the general case. Finally, a sparse extension of the Fisher-EM algorithm is proposed in order to perform a selection of the original variables which are discriminative.
Infos
- Laure Guitton (lguitton)
-
- Université Paris 1 Panthéon - Sorbonne (production)
- Camille Brunet (Intervenant)
- 21 juillet 2017 00:00
- Cours / MOOC / SPOC
- Anglais
