xPerT: Extended Persistence Transformer

TL;DR

The paper introduces xPerT, a scalable transformer architecture for persistence diagrams that significantly reduces memory usage and enhances accuracy without complex preprocessing or tuning.

Contribution

xPerT is a novel, scalable transformer model for persistence diagrams that outperforms existing methods in memory efficiency and accuracy.

Findings

Reduces GPU memory usage by over 90%.

Improves accuracy on multiple datasets.

Requires no complex preprocessing or extensive hyperparameter tuning.

Abstract

A persistence diagram provides a compact summary of persistent homology, which captures the topological features of a space at different scales. However, due to its nature as a set, incorporating it as a feature into a machine learning framework is challenging. Several methods have been proposed to use persistence diagrams as input for machine learning models, but they often require complex preprocessing steps and extensive hyperparameter tuning. In this paper, we propose a novel transformer architecture called the \textit{Extended Persistence Transformer (xPerT)}, which is highly scalable than the compared to Persformer, an existing transformer for persistence diagrams. xPerT reduces GPU memory usage by over 90\% and improves accuracy on multiple datasets. Additionally, xPerT does not require complex preprocessing steps or extensive hyperparameter tuning, making it easy to use in practice. Our code is available at https://github.com/sehunfromdaegu/xpert.