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A Survey of Vectorization Methods in Topological Data Analysis

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arxiv 2212.09703 v1 pith:LDQE5EJY submitted 2022-12-19 math.AT

Dashti Ali , Aras Asaad , Maria-Jose Jimenez , Vidit Nanda , Eduardo Paluzo-Hidalgo , Manuel Soriano-Trigueros This is my paper · ORCID
classification math.AT
keywords methodsvectorizationtaskstopologicalanalysisapplicationattemptsbarcodes
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Attempts to incorporate topological information in supervised learning tasks have resulted in the creation of several techniques for vectorizing persistent homology barcodes. In this paper, we study thirteen such methods. Besides describing an organizational framework for these methods, we comprehensively benchmark them against three well-known classification tasks. Surprisingly, we discover that the best-performing method is a simple vectorization, which consists only of a few elementary summary statistics. Finally, we provide a convenient web application which has been designed to facilitate exploration and experimentation with various vectorization methods.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. TopoFormer: Topology Meets Attention for Graph Learning

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Sliding-window interlevel Betti sequences (Topo-Scan) plus Transformers match or beat strong GNN and TDA baselines on graph classification and molecular property tasks while avoiding full persistence diagrams.

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