# On the choice of weight functions for linear representations of persistence diagrams

arXiv: https://arxiv.org/abs/1807.03678 · HTML: https://mlsanity.com/paper/1807.03678/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Estimation and Quantization of Expected Persistence Diagrams](https://mlsanity.com/paper/2105.04852/) (2021)
- [Approximating Persistent Homology for Large Datasets](https://mlsanity.com/paper/2204.09155/) (2022)
- [Topology Applied to Machine Learning: From Global to Local](https://mlsanity.com/paper/2103.05796/) (2021)
- [How much of persistent homology is topology? A quantitative decomposition for spin model phase transitions](https://mlsanity.com/paper/2603.29072/) (2026)
- [Towards Scalable Topological Regularizers](https://mlsanity.com/paper/2501.14641/) (2025)
- [Topological Signatures of Adversaries in Multimodal Alignments](https://mlsanity.com/paper/2501.18006/) (2025)
- [Persistence diagrams of random matrices via Morse theory: universality and a new spectral diagnostic](https://mlsanity.com/paper/2603.27903/) (2026)
- [Denoising 3D images: robustness of persistent homology measures](https://mlsanity.com/paper/2607.24579/) (2026)
- [Spectral origin of the topological gap exponent d + η: mechanism, kernel, decomposition, and scope](https://mlsanity.com/paper/2609.09159/) (2026)

## Abstract (arXiv)

Persistence diagrams are efficient descriptors of the topology of a point cloud. As they do not naturally belong to a Hilbert space, standard statistical methods cannot be directly applied to them. Instead, feature maps (or representations) are commonly used for the analysis. A large class of feature maps, which we call linear, depends on some weight functions, the choice of which is a critical issue. An important criterion to choose a weight function is to ensure stability of the feature maps with respect to Wasserstein distances on diagrams. We improve known results on the stability of such maps, and extend it to general weight functions. We also address the choice of the weight function by considering an asymptotic setting; assume that mathbbXn is an i.i.d. sample from a density on [0,1]d. For the vCech and Rips filtrations, we characterize the weight functions for which the corresponding feature maps converge as n approaches infinity, and by doing so, we prove laws of large numbers for the total persistences of such diagrams. Those two approaches (stability and convergence) lead to the same simple heuristic for tuning weight functions: if the data lies near a d-dimensional manifold, then a sensible choice of weight function is the persistence to the power α with α ≥ d.