TOAST: Topological Algorithm for Singularity Tracking

The manifold hypothesis, which assumes that data lie on or close to an unknown manifold of low intrinsic dimensionality, is a staple of modern machine learning research. However, recent work has shown that realworld data exhibit distinct non-manifold structures, which result in singularities that can lead to erroneous conclusions about the data. Detecting such singularities is therefore crucial as a precursor to interpolation and inference tasks. We address detecting singularities by developing (i) persistent local homology, a new topologydriven framework for quantifying the intrinsic dimension of a data set locally, and (ii) Euclidicity, a topologybased multi-scale measure for assessing the ‘manifoldness’ of individual points. We show that our approach can reliably identify singularities of complex spaces, while also capturing singular structures in real-world data sets.

https://arxiv.org/abs/2210.00069