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Title A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams
Authors Rolando Kindelan, Mircea Petrache, Mauricio Cerda, Nancy Hitschfeld
Publication date 2024
Abstract Persistence diagrams (PD)s play a central role in
topological
data analysis, and are used in an ever increasing variety of
applications. The comparison of PD data requires computing
comparison metrics among large sets of PDs, with metrics
which are accurate, theoretically sound, and fast to compute.
Especially for denser multi-dimensional PDs, such compari-
son metrics are lacking. While on the one hand, Wasserstein-
type distances have high accuracy and theoretical guarantees,
they incur high computational cost. On the other hand, dis-
tances between vectorizations such as Persistence Statistics
(PS)s have lower computational cost, but lack the accuracy
guarantees and in general they are not guaranteed to dis-
tinguish PDs (i.e. the two PS vectors of different PDs may
be equal). In this work we introduce a class of pseudodis-
tances called Extended Topological Pseudodistances (ETD)s,
which have tunable complexity, and can approximate Sliced
and classical Wasserstein distances at the high-complexity
extreme, while being computationally lighter and close to
Persistence Statistics at the lower complexity extreme, and
thus allow users to interpolate between the two metrics. We
build theoretical comparisons to show how to fit our new dis-
tances at an intermediate level between persistence vector-
izations and Wasserstein distances. We also experimentally
verify that ETDs outperform PSs in terms of accuracy and
outperform Wasserstein and Sliced
Pages 13202-13210
Conference name Annual AAAI Conference on Artificial Intelligence
Publisher Association for the Advancement of Artificial Intelligence (www.aaai.org)
Reference URL View reference page