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Algebraic Machine Learning with an Application to Chemistry
Feb. 20, 2024, 5:44 a.m. | Ezzeddine El Sai, Parker Gara, Markus J. Pflaum
cs.LG updates on arXiv.org arxiv.org
Abstract: As datasets used in scientific applications become more complex, studying the geometry and topology of data has become an increasingly prevalent part of the data analysis process. This can be seen for example with the growing interest in topological tools such as persistent homology. However, on the one hand, topological tools are inherently limited to providing only coarse information about the underlying space of the data. On the other hand, more geometric approaches rely predominately …
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