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.
Keywords
- Convex Hull
- Convex Combination
- Natural Image
- Nonnegative Matrix Factorization
- Nonnegative Matrix
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Bauckhage, C., Thurau, C. (2009). Making Archetypal Analysis Practical. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_28
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DOI: https://doi.org/10.1007/978-3-642-03798-6_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03797-9
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