提出xPerT模型，高效处理拓扑数据并大幅降低显存占用。
xPerT: Extended Persistence Transformer
- 基于Transformer架构直接处理持久性图谱，无需复杂预处理。
- 显存使用减少90%以上，多数据集上准确率提升显著。
- 适合需要低资源、易部署的拓扑数据分析场景。
持久性图谱能紧凑地概括空间在不同尺度下的拓扑特征，但因其为集合结构，难以直接融入机器学习框架。现有方法常需复杂预处理和大量超参数调优。本文提出新型Transformer模型xPerT，相比已有方法Persformer更具可扩展性。xPerT将GPU内存占用降低超过90%，并在多个数据集上提升准确率。该模型无需复杂预处理或繁复调参，实际应用便捷。代码已开源：https://github.com/sehunfromdaegu/xpert。
原文摘要 · Abstract (English)
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.
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