Multidimensional Persistence Module Classification via Lattice-Theoretic Convolutions
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.PDF Abstract NeurIPS Workshop 2020 PDF NeurIPS Workshop 2020 Abstract
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