Institutional Repository
Technical University of Crete
Technical University of Crete
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EL
| URI: | http://purl.tuc.gr/dl/dias/DD112E37-A1C2-4866-AA4A-8B688F8AC72B | ||
| Year | 2022 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | D. Leykam, I. Rondón and D. G. Angelakis, “Dark soliton detection using persistent homology,” Chaos, vol. 32, no. 7, July 2022, doi: 10.1063/5.0097053. https://doi.org/10.1063/5.0097053 | ||
| Appears in Collections |
Classifying images often requires manual identification of qualitative features. Machine learning approaches including convolutional neural networks can achieve accuracy comparable to human classifiers but require extensive data and computational resources to train. We show how a topological data analysis technique, persistent homology, can be used to rapidly and reliably identify qualitative features in experimental image data. The identified features can be used as inputs to simple supervised machine learning models, such as logistic regression models, which are easier to train. As an example, we consider the identification of dark solitons using a dataset of 6257 labeled atomic Bose–Einstein condensate density images.
Copyright © DIAS 2013
