In this article, we introduce the notion of "m-dimensional n-principal points," which is a generalization of the notion of n-principal points. A set of m-dimensional n-principal points of a distribution is defined as a set of n points that optimally represents the distribution in terms of mean squared distance subject to the condition that the dimension of the linear subspace spanned by the n points is at most m. Its properties and connections to principal components are investigated for elliptically symmetric distributions and a location mixture of spherically symmetric distributions.
- Elliptically symmetric distribution
- Location mixture
- Mean squared distance
- Principal points
- Spherically symmetric distribution
ASJC Scopus subject areas
- Statistics and Probability