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• W2940926677 abstract "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, the choice of the weight function should depend on the nature of the specific type of data one considers, and it is thus highly desirable to learn a best weight function (and thus metric for persistence diagrams) from labelled data. We study this problem and develop a new weighted kernel, called WKPI, for persistence summaries, as well as an optimization framework to learn a good metric for persistence summaries. Both our kernel and optimization problem have nice properties. We further apply the learned kernel to the challenging task of graph classification, and show that our WKPI-based classification framework obtains similar or (sometimes significantly) better results than the best results from a range of previous graph classification frameworks on a collection of benchmark datasets." @default.
• W2940926677 created "2019-05-03" @default.
• W2940926677 creator A5020217603 @default.
• W2940926677 creator A5030187457 @default.
• W2940926677 date "2019-04-27" @default.
• W2940926677 modified "2023-09-27" @default.
• W2940926677 title "Learning metrics for persistence-based summaries and applications for graph classification" @default.
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