Linear-Size Approximations to the Vietoris-Rips Filtration

Presented at The Symposium on Computational Geometry 2012, University of North Carolina Chapel Hill

The Vietoris-Rips filtration is a versatile tool in topological data analysis.
Unfortunately, it is often too large to construct in full.
We show how to construct an $O(n)$-size filtered simplicial complex on an $n$-point metric space such that the persistence diagram is a good approximation to that of the Vietoris-Rips filtration.
The filtration can be constructed in $O(n\log n)$ time.
The constants depend only on the doubling dimension of the metric space and the desired tightness of the approximation.
For the first time, this makes it computationally tractable to approximate the persistence diagram of the Vietoris-Rips filtration across all scales for large data sets.

Our approach uses a hierarchical net-tree to sparsify the filtration. We can either sparsify the data by throwing out points at larger scales to give a zigzag filtration, or sparsify the underlying graph by throwing out edges at larger scales to give a standard filtration. Both methods yield the same guarantees.

Our approach uses a hierarchical net-tree to sparsify the filtration. We can either sparsify the data by throwing out points at larger scales to give a zigzag filtration, or sparsify the underlying graph by throwing out edges at larger scales to give a standard filtration. Both methods yield the same guarantees.