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Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions
November 28, 2020 · Declared Dead · 🏛 arXiv.org
"No code URL or promise found in abstract"
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Authors
Hans Riess, Jakob Hansen, Robert Ghrist
arXiv ID
2011.14057
Category
math.AT
Cross-listed
cs.LG,
eess.SP
Citations
5
Venue
arXiv.org
Last Checked
1 month ago
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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