Persistence kernels for classification: A comparative study
This work addresses the problem of selecting effective kernels for topological data analysis in classification, but it is incremental as it focuses on comparison rather than introducing new methods.
The paper conducted a comparative study of five persistence kernels for classification tasks, evaluating their performance on various datasets and providing Python code for reproducibility.
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.