Learning Extremal Representations with Deep Archetypal Analysis

@article{Keller2021LearningER,
  title={Learning Extremal Representations with Deep Archetypal Analysis},
  author={Sebastian Mathias Keller and Maxim Samarin and Fabricio Arend Torres and Mario Wieser and Volker Roth},
  journal={International Journal of Computer Vision},
  year={2021},
  volume={129},
  pages={805 - 820}
}
Archetypes represent extreme manifestations of a population with respect to specific characteristic traits or features. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. As mixing of archetypes is performed directly on the input data, linear Archetypal Analysis requires additivity of the input, which is a strong assumption unlikely to hold e.g. in case of image data. To address this problem, we propose… 

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Archetypal analysis for machine learning and data mining

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