Principal Components are usually hard to interpret. Sparseness is considered as one way to improve interpretability, and thus a trade-off between variance explained by the components and sparseness is frequently sought. In this note we address the problem of simultaneous maximization of variance...

In this note we present a characterization of halfspace depth which relates it with well-known concepts of Locational Analysis. This characterization also leads to a natural extension of the concept of depth to noneuclidean location estimation as well as other settings like regression.