Fast and Robust Archetypal Analysis for Representation Learning
This work addresses the limited adoption of archetypal analysis in scientific problems due to implementation barriers, offering a practical solution for researchers in fields like computer vision.
The authors tackled the inefficiency and lack of accessible implementations of archetypal analysis, an unsupervised learning technique, by developing a fast optimization scheme with an active-set strategy and providing an open-source implementation, demonstrating its application in computer vision tasks like codebook learning and signal classification.
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.