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Learning metrics for persistence-based summaries and applications for graph classification
[article]
2019
arXiv
pre-print
Recently a new feature representation and data analysis methodology based on a topological tool called persistent homology (and its corresponding persistence diagram summary) has started to attract momentum. A series of methods have been developed to map a persistence diagram to a vector representation so as to facilitate the downstream use of machine learning tools, and in these approaches, the importance (weight) of different persistence features are often preset. However often in practice,
arXiv:1904.12189v2
fatcat:e5l5nh5pbzhodnwwwtvnekwqwq