🍩 Database of Original & Non-Theoretical Uses of Topology

1. Topological Singularity Detection at Multiple Scales (2023)

   Julius von Rohrscheidt, Bastian Rieck

   Abstract

   The manifold hypothesis, which assumes that data lies on or close to an unknown manifold of low intrinsic dimension, is a staple of modern machine learning research. However, recent work has shown that real-world data exhibits distinct non-manifold structures, i.e. singularities, that can lead to erroneous findings. Detecting such singularities is therefore crucial as a precursor to interpolation and inference tasks. We address this issue by developing a topological framework that (i) quantifies the local intrinsic dimension, and (ii) yields a Euclidicity score for assessing the ’manifoldness’ of a point along multiple scales. Our approach identifies singularities of complex spaces, while also capturing singular structures and local geometric complexity in image data.

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   • Data analysis
   • Machine learning
   • Manifold learning
   • Visualization
   • Point cloud
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