Fast and Robust Archetypal Analysis for Representation Learning

Yuansi Chen 1, 2 Julien Mairal 2 Zaid Harchaoui 2
2 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We revisit a pioneer unsupervised learning technique called archetypal analysis, which is related to successful data analysis methods such as sparse coding and non-negative matrix factorization. Since it was proposed, archetypal analysis did not gain a lot of popularity even though it produces more interpretable models than other alternatives. Because no efficient implementation has ever been made publicly available, its application to important scientific problems may have been severely limited. Our goal is to bring back into favour archetypal analysis. We propose a fast optimization scheme using an active-set strategy, and provide an efficient open-source implementation interfaced with Matlab, R, and Python. Then, we demonstrate the usefulness of archetypal analysis for computer vision tasks, such as codebook learning, signal classification, and large image collection visualization.
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Yuansi Chen, Julien Mairal, Zaid Harchaoui. Fast and Robust Archetypal Analysis for Representation Learning. CVPR 2014 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2014, Columbus, United States. pp.1478-1485, ⟨10.1109/CVPR.2014.192⟩. ⟨hal-00995911⟩

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