Persistence kernels for classification: A comparative study

Fuente: arXiv
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Main Authors: Bandiziol, Cinzia, De Marchi, Stefano
Format: Preprint
Published: 2024
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author Bandiziol, Cinzia
De Marchi, Stefano
author_facet Bandiziol, Cinzia
De Marchi, Stefano
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2408_07090
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Persistence kernels for classification: A comparative study
Bandiziol, Cinzia
De Marchi, Stefano
Machine Learning
Algebraic Topology
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.
title Persistence kernels for classification: A comparative study
topic Machine Learning
Algebraic Topology
url https://arxiv.org/abs/2408.07090