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Convolutional persistence transforms

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Abstract

In this paper, we consider topological featurizations of data defined over simplicial complexes, like images and labeled graphs, obtained by convolving this data with various filters before computing persistence. Viewing a convolution filter as a local motif, the persistence diagram of the resulting convolution describes the way the motif is distributed across the simplicial complex. This pipeline, which we call convolutional persistence, extends the capacity of topology to observe patterns in such data. Moreover, we prove that (generically speaking) for any two labeled complexes one can find some filter for which they produce different persistence diagrams, so that the collection of all possible convolutional persistence diagrams is an injective invariant. This is proven by showing convolutional persistence to be a special case of another topological invariant, the Persistent Homology Transform. Other advantages of convolutional persistence are improved stability, greater flexibility for data-dependent vectorizations, and reduced computational complexity for certain data types. Additionally, we have a suite of experiments showing that convolutions greatly improve the predictive power of persistence on a host of classification tasks, even if one uses random filters and vectorizes the resulting diagrams by recording only their total persistences.

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Notes

  1. The main theorem of (Cohen-Steiner et al. 2007) is much more general, applying to tame functions on triangulable spaces, conditions which are automatically satisfied in the simplicial setting.

  2. The symbol \(\odot \) refers to the Hadamard product, which performs componentwise multiplication between vectors.

  3. See (Hewitt and Ross 2012), Theorem 20.18 on page 296, for a proof of Young’s inequality for locally compact groups, which in particular includes \(\mathbb {Z}^d\).

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Authors and Affiliations

  1. Mathematics, Duke University, Durham, NC, USA

    Yitzchak Elchanan Solomon & Paul Bendich

  2. Geometric Data Analytics, Durham, NC, USA

    Paul Bendich

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  1. Yitzchak Elchanan Solomon
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Correspondence to Yitzchak Elchanan Solomon.

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The authors were partially supported by the Air Force Office of Scientific Research under the grant “Geometry and Topology for Data Analysis and Fusion”, AFOSR FA9550-18-1-0266. They would also like to extend their thanks to Alexander Wagner, for helpful conversations.

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Solomon, Y.E., Bendich, P. Convolutional persistence transforms. J Appl. and Comput. Topology (2024). https://doi.org/10.1007/s41468-024-00164-x

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