Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions
Algebraic Topology
2022-09-01 v2 Machine Learning
Signal Processing
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.
Cite
@article{arxiv.2011.14057,
title = {Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions},
author = {Hans Riess and Jakob Hansen and Robert Ghrist},
journal= {arXiv preprint arXiv:2011.14057},
year = {2022}
}