We introduce a new notion of central axis for a finite set (Formula presented.) of vectors in (Formula presented.). In tandem, we discuss different ways of measuring the dispersion of the data points (Formula presented.)'s around the central axis. Finally, we explain how to detect numerically the most peripheral points of the given dataset.

Central axes and peripheral points in high dimensional directional datasets

Astorino A;
2016

Abstract

We introduce a new notion of central axis for a finite set (Formula presented.) of vectors in (Formula presented.). In tandem, we discuss different ways of measuring the dispersion of the data points (Formula presented.)'s around the central axis. Finally, we explain how to detect numerically the most peripheral points of the given dataset.
2016
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Central axis of a dataset
Conic barycenter
Conic circumcenter
Convex optimization
Outliers detection
Peripheral point
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/286617
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