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Towards a Persistence Diagram that is Robust to Noise and Varied Densities

conference contribution
posted on 2023-11-22, 04:50 authored by H Zhang, K Zhang, KM Ting, Ye ZhuYe Zhu
Recent works have identified that existing methods, which construct persistence diagrams in Topological Data Analysis (TDA), are not robust to noise and varied densities in a point cloud. We analyze the necessary properties of an approach that can address these two issues, and propose a new filter function for TDA based on a new data-dependent kernel which possesses these properties. Our empirical evaluation reveals that the proposed filter function provides a better means for t-SNE visualization and SVM classification than three existing methods of TDA.

History

Volume

202

Pagination

41952-41972

eISSN

2640-3498

Title of proceedings

Proceedings of Machine Learning Research

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