2.3 Discriminative clustering for high-dimensional data

2 janvier 2017
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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.