ArticleFrontiers in artificial intelligence2021
Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's Guide.
Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains.Journal of chemical theory and computation · 2025Article
- Tight basis cycle representatives for persistent homology of large biological data sets.PLoS computational biology · 2023Article
- Homology of homologous knotted proteins.Journal of the Royal Society, Interface · 2023Article
- Minimal Cycle Representatives in Persistent Homology Using Linear Programming: An Empirical Study With User's Guide.Frontiers in artificial intelligence · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cycle representatives of persistent homology classes can be used to provide descriptions of topological features in data. However, the non-uniqueness of these representatives creates ambiguity and can lead to many different interpretations of the same set of classes. One approach to solving this problem is to optimize the choice of representative against some measure that is meaningful in the context of the data. In this work, we provide a study of the effectiveness and computational cost of several
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