Summaries of persistence diagrams and their applications

Summaries of persistence diagrams and their applications

Applied Math And Analysis Seminar

Yu-Min Chung (UNC)

Wednesday, October 30, 2019 -
12:00pm to 1:00pm
119 Physics

Persistence diagrams are one of the main tools in the field of Topological Data Analysis (TDA). They contain fruitful information about the shape of data. The use of machine learning algorithms on the space of persistence diagrams proves to be challenging as the space is complicated. For that reason, transforming these diagrams in a way that is compatible with machine learning is an important topic currently researched in TDA. In this talk, we propose two summaries: persistence statistics and persistence curves. To demonstrate their effectivenesses, their applications to sleep stage analysis (joint work with Hau-Tieng Wu, and Yu-Lun Lo), and texture classification (joint work with Austin Lawson), will be presented. The stability property will also be discussed.

Last updated: 2020/02/20 - 2:34pm