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2019 | OriginalPaper | Chapter

Deep Archetypal Analysis

Authors : Sebastian Mathias Keller, Maxim Samarin, Mario Wieser, Volker Roth

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Deep Archetypal Analysis (DeepAA) generates latent representations of high-dimensional datasets in terms of intuitively understandable basic entities called archetypes. The proposed method extends linear Archetypal Analysis (AA), an unsupervised method to represent multivariate data points as convex combinations of extremal data points. Unlike the original formulation, Deep AA is generative and capable of handling side information. In addition, our model provides the ability for data-driven representation learning which reduces the dependence on expert knowledge. We empirically demonstrate the applicability of our approach by exploring the chemical space of small organic molecules. In doing so, we employ the archetype constraint to learn two different latent archetype representations for the same dataset, with respect to two chemical properties. This type of supervised exploration marks a distinct starting point and let us steer de novo molecular design.

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Appendix
Available only for authorised users
Footnotes
1
Note that \(i=1..m\) (and not up to n), which reflects that deep neural networks usually require batch-wise training with batch size m.
 
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Metadata
Title
Deep Archetypal Analysis
Authors
Sebastian Mathias Keller
Maxim Samarin
Mario Wieser
Volker Roth
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33676-9_12

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