In this article, we study Euler characteristic techniques in topological data analysis. Pointwise computing the Euler characteristic of a family of simplicial complexes built from data gives rise to the so-called Euler characteristic profile. We show that this simple descriptor achieve state-of-the-art performance in supervised tasks at a very low computational cost. Inspired by signal analysis, we compute hybrid transforms of Euler characteristic profiles. These integral transforms mix Euler characteristic techniques with Lebesgue integration to provide highly efficient compressors of topological signals. As a consequence, they show remarkable performances in unsupervised settings. On the qualitative side, we provide numerous heuristics on the topological and geometric information captured by Euler profiles and their hybrid transforms. Finally, we prove stability results for these descriptors as well as asymptotic guarantees in random settings.

翻译：本文研究拓扑数据分析中的欧拉特征技术。通过逐点计算从数据构建的单纯复形族的欧拉特征，可得到所谓的欧拉特征剖面。我们证明这种简单的描述符能以极低的计算成本在监督任务中达到最先进的性能。受信号分析启发，我们计算欧拉特征剖面的混合变换。这些积分变换将欧拉特征技术与勒贝格积分相结合，为拓扑信号提供了高效的压缩器。因此，它们在无监督场景中表现出卓越的性能。在定性分析方面，我们提出了大量关于欧拉剖面及其混合变换所捕获的拓扑与几何信息的启发式方法。最后，我们证明了这些描述符的稳定性结果以及在随机设置中的渐近保证。