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arXiv · 2011.14057

Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions

Hans Riess·Jakob Hansen·Robert Ghrist
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Abstract

Multiparameter persistent homology has been largely neglected as an input to machine learning algorithms. We consider the use of lattice-based convolutional neural network layers as a tool for the analysis of features arising from multiparameter persistence modules. We find that these show promise as an alternative to convolutions for the classification of multidimensional persistence modules.

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BibTeXRIS

Hans Riess, Jakob Hansen, Robert Ghrist. 2020-11-28. Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions. https://arxiv.org/abs/2011.14057

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