Persistence kernels for classification: A comparative study
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Abstract (arXiv)
The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels that are then used to compare their performances of classification on various datasets. We also provide the Python codes for the reproducibility of results.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 42 references that are in the map. First posted on arXiv 9 Aug 2024.
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References · 11
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2015Persistence Images: A Stable Vector Representation of Persistent Homologycited by 235
- 2020Metrics for Multi-Class Classification: an Overviewcited by 140
- 2017Sliced Wasserstein Kernel for Persistence Diagramscited by 77
- 2014A Stable Multi-Scale Kernel for Topological Machine Learningcited by 74
- 2019Learning metrics for persistence-based summaries and applications for graph classificationcited by 57
- 2018Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagramscited by 33
- 2018Topology-Aware Surface Reconstruction for Point Cloudscited by 21
- 2019A Topological "Reading" Lesson: Classification of MNIST using TDAcited by 21
- 2017Kernel method for persistence diagrams via kernel embedding and weight factorcited by 20
- 2022Persistent Homology for Breast Tumor Classification using Mammogram Scanscited by 4
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