Fast and Robust Archetypal Analysis for Representation Learning

By Yuansi Chen, Julien Mairal, Zaïd Harchaoui. 2014

124 citations recorded by the source corpus.

We revisit a pioneer unsupervised learning technique called archetypal analysis, [5] which is related to successful data analysis methods such as sparse coding [18] and non-negative matrix factorization [19]. 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.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Prototypal Analysis and Prototypal Regression (2017, background): “This extra computational cost can be handled efficiently, as several studies have shown (Bauckhage and Thurau, 2009; Mørup and Hansen, 2012; Chen et al., 2014).”
Sparse Modeling for Image and Vision Processing (2014, methodology): “Even though the `1-norm was introduced for that purpose in geophysics [Claerbout and Muir, 1973, Taylor et al., 1979], it was popularized in statistics with the Lasso estimator of Tibshirani [1996] and independently in signal processing with the basis pursuit formulation of Chen et al.”
TVSum: Summarizing web videos using titles (2015, methodology): “To demonstrate the effectiveness of our method in an objective manner, we performed an experiment on synthetic data, comparing our approach to archetypal analysis [11].”