PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Making Archetypal Analysis Practical
Christian Bauckhage and Christian Thurau
In: DAGM 2009, Jena, Germany(2009).

Abstract

Archetypal analysis represents the members of a set of multivariate data as a convex combination of extremal points of the data. It allows for dimensionality reduction and clustering and is particularly useful whenever the data are superpositions of basic entities. However, since its computation costs grow quadratically with the number of data points, the original algorithm hardly applies to modern pattern recognition or data mining settings. In this paper, we introduce ways of notably accelerating archetypal analysis. Our experiments are the first successful application of the technique to large scale data analysis problems.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6506
Deposited By:Christian Thurau
Deposited On:08 March 2010