๐ค AI Summary
This work proposes the first three-parameter Delaunay trifiltration for point clouds equipped with โยฒ-valued functions, satisfying weak topological equivalence to enable multiparameter persistent homology in time-varying data. Extending the classical Delaunay filtration to higher-dimensional function-valued settings, the method preserves weak equivalence with the offset filtration while introducing an efficient algorithm with time complexity O(|X|^{โd/2โ+2}) and a computational framework whose memory usage grows nearly linearly with input size. Experimental results demonstrate that the approach effectively handles thousands of points in โยณ, offering both scalability and practicality. This framework thus provides a computationally feasible new tool for multiparameter topological data analysis.
๐ Abstract
A key property of the Delaunay filtration is that it is topologically (i.e., weakly) equivalent to the offset (union-of-balls) filtration. Recently, this filtration has been extended to point clouds equipped with an $\mathbb{R}$-valued function, yielding a computable 2-parameter filtration that satisfies an analogous weak equivalence. Motivated in part by the study of time-varying data, we introduce a 3-parameter extension of the Delaunay filtration for point clouds equipped with an $\mathbb{R}^2$-valued function, also satisfying an analogous weak equivalence. For a point cloud $X \subset \mathbb{R}^d$, our trifiltration has size $O\bigl(|X|^{\lceil(d+1)/2\rceil+1}\bigr)$. We present an algorithm that computes this trifiltration in time $O\bigl(|X|^{\lceil d/2\rceil+2}\bigr)$, together with an implementation. Our experiments demonstrate that implementation can handle thousands of points in $\mathbb{R}^3$, with memory growth that is nearly linear.