Persformer: A Transformer Architecture for Topological Machine Learning

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 textttPersformer, the first Transformer neural network architecture that accepts persistence diagrams as input. The textttPersformer architecture significantly outperforms previous topological neural network architectures on classical synthetic and graph benchmark datasets. Moreover, it satisfies a universal approximation theorem. This allows us to introduce the first interpretability method for topological machine learning, which we explore in two examples.