Central axes and peripheral points in high dimensional directional datasets

We introduce a new notion of central axis for a finite set $\{a_1,\dots ,a_m\}$ of vectors in ${‐{R}}^n$ . In tandem, we discuss different ways of measuring the dispersion of the data points $a_i$ ‘s around the central axis. Finally, we explain how to detect numerically the most peripheral points of the given dataset.