Persformer: A Transformer Architecture for Topological Machine Learning. (arXiv:2112.15210v1 [cs.LG])

One of the main challenges of Topological Data Analysis (TDA) is to extract
features from persistent diagrams directly usable by machine learning
algorithms. Indeed, persistence diagrams are intrinsically (multi-)sets of
points in R2 and cannot be seen in a straightforward manner as vectors. In this
article, we introduce Persformer, the first Transformer neural network
architecture that accepts persistence diagrams as input. The Persformer
architecture significantly outperforms previous topological neural network
architectures on classical synthetic benchmark datasets. Moreover, it satisfies
a …

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