Multidimensional Persistence Module Classification via Lattice-Theoretic ConvolutionsDownload PDF

Oct 10, 2020 (edited Dec 02, 2020)NeurIPS 2020 Workshop TDA and Beyond Blind SubmissionReaders: Everyone
  • Keywords: persistent homology, multidimensional persistence, neural networks, lattice theory, signal processing
  • TL;DR: Lattice-based convolutional networks beat classical convolutional networks in classifying (certain) 2d persistence modules.
  • 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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