Persistent Homology in Machine Learning: Applied Sciences Review

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dc.contributor.authorYavorskyi, Oleksandr
dc.contributor.authorAsseko-Nkili, Andrii
dc.contributor.authorKussul, Nataliia
dc.date.accessioned2023-11-07T13:36:58Z
dc.date.available2023-11-07T13:36:58Z
dc.date.issued2023
dc.description.abstractTopological Data Analysis (‘TDA’) has become a vibrant and quickly developing field in recent years, providing topology-enhanced data processing and Machine Learning (‘ML’) applications. Due to the novelty of the field, as well as the dissimilarity between the mathematics behind the classical ML and TDA, it might be complicated for a field newcomer to assess the feasibility of the approaches proposed by TDA and the relevancy of the possible applications. The current paper aims to provide an overview of the recent developments that relate to persistent homology, a part of the mathematical machinery behind the TDA, with a particular focus on applied sciences. We consider multiple areas, such as physics, healthcare, material sciences, and others, examining the recent developments in the field. The resulting summary of this paper could be used by field experts to expand their knowledge on recent persistent homology applications, while field newcomers could assess the applicability of this TDA approach for their research. We also point out some of the current restrictions on the use of persistent homology, as well as potential development trajectories that might be useful to the whole field.uk
dc.format.pagerangePp. 61-66uk
dc.identifier.citationYavorskyi, O. Persistent Homology in Machine Learning: Applied Sciences Review / Oleksandr Yavorskyi, Andrii Asseko-Nkili, Nataliia Kussul // In Proceedings of International Conference on Applied Innovation in IT, (ICAIIT). – 2023. – Pp. 61-66. – Bibliogr.: 29 ref.uk
dc.identifier.doihttps://doi.org/10.25673/101914
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62046
dc.language.isoenuk
dc.relation.ispartofProceedings of the 11th International Conference on Applied Innovations in IT, (ICAIIT)uk
dc.subjectAlgebraic Topologyuk
dc.subjectPersistent Homologyuk
dc.subjectMachine Learninguk
dc.subjectPhysicsuk
dc.subjectHealthcareuk
dc.subjectTopological Data Analysisuk
dc.subjectChemistryuk
dc.subjectBiologyuk
dc.subjectMaterial Sciencesuk
dc.subjectData Processinguk
dc.titlePersistent Homology in Machine Learning: Applied Sciences Reviewuk
dc.typeArticleuk

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