Finite mixtures, projection pursuit and tensor rank: a triangulation

Finite mixtures of multivariate distributions play a fundamental role in model-based clustering. However, they pose several problems, especially in the presence of many irrelevant variables. Dimension reduction methods, such as projection pursuit, are commonly used to address these problems. In this paper, we use skewness-maximizing projections to recover the subspace which optimally separates the cluster means. Skewness might then be removed in order to search for other potentially interesting data structures or to perform skewness-sensitive statistical analyses, such as the Hotelling’s $$ T^{2}$$T2 test. Our approach is algebraic in nature and deals with the symmetric tensor rank of the third multivariate cumulant. We also derive closed-form expressions for the symmetric tensor rank of the third cumulants of several multivariate mixture models, including mixtures of skew-normal distributions and mixtures of two symmetric components with proportional covariance matrices. Theoretical results in this paper shed some light on the connection between the estimated number of mixture components and their skewness.