Clustering in Fisher Discriminative Subspaces
Charles Bouveyron, Camille Brunet · 2009
Abstract. Clustering in high-dimensional spaces is nowadays a recurrent problem in many scientific domains but remains a difficult problem. This is mainly due to the fact that high-dimensional data usually live in low-dimensional subspaces hid-den in the original space. This paper presents a model-based clustering approach which models the data in a discriminative subspace with an intrinsic dimension lower than the dimension of the original space. An estimation algorithm, called Fisher-EM algorithm, is proposed for estimating both the mixture parameters and the discriminative subspace. Experiments show that the proposed approach out-performs existing clustering methods and provides a useful representation of the high-dimensional data.