Nearest archetype hull methods for large-scale data classification

This paper introduces an efficient geometric approach for data classification that can build class models from large amounts of high dimensional data. We determine a convex model of the data as the outcome of convex hull non-negative matrix factorization, a large-scale variant of Archetypal Analysis. The resulting convex regions or archetype hulls give an optimal (in a least squares sense) bounding of the data region and can be efficiently computed. We classify based on the minimum distance to the closest archetype hull. The proposed method offers (i) an intuitive geometric interpretation, (ii) single as well as multi-class classification, and (iii) handling of large amounts of high dimensional data. Experimental evaluation on common benchmark data sets shows promising results