Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Making large-scale support vector machine learning practical
Advances in kernel methods
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Principal Components of Natural Images Revisited
IEEE Transactions on Pattern Analysis and Machine Intelligence
80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted and robust archetypal analysis
Computational Statistics & Data Analysis
Archetypal analysis for machine learning and data mining
Neurocomputing
Expert Systems with Applications: An International Journal
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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.