Non-isotropic Persistent Homology
Keywords: Machine Learning, TDA, Persistent Homology, Geometric Learning, Geometry, Topology, Optimal Transport
TL;DR: NIPH extracts topological and geometrical information by varying the distance function on the underlying space.
Abstract: Persistent Homology is a widely used topological data analysis tool that creates a concise description of the topological properties of a point cloud based on a specified filtration.
Most of these filtrations used for persistent homology depend (implicitly) on a chosen metric, which is typically agnostically chosen as the standard euclidean metric on $\mathbb{R}^n$.
Recent work has tried to uncover the "true" metric on the point cloud using distance-to-measure functions, in order to obtain more meaningful persistent homology results.
Here we propose an alternative look at this problem:
we posit that information on the point cloud is lost when restricting persistent homology to a single (correct) distance function. Instead, we show how by varying the distance function on the underlying space and analysing the corresponding shifts in the persistence diagrams, we can extract additional topological and geometrical information.
Finally, we show in synthetic experiments that non-isotropic persistent homology (NIPH) can extract information on orientation, orientational variance, and scaling of randomly generated point clouds with good accuracy.
Supplementary Materials: zip
Type Of Submission: Extended Abstract (4 pages, non-archival)
Submission Number: 62
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