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Rapid and Precise Topological Comparison with Merge Tree Neural Networks
To address this challenge, we introduce the merge tree neural networks (MTNN), a learned neural network model designed for merge tree comparison.
Visualizing Topological Importance: A Class-Driven Approach
This paper presents the first approach to visualize the importance of topological features that define classes of data.
Distance Measures for Geometric Graphs
We study two notions of distance measures for geometric graphs, called the geometric edit distance (GED) and geometric graph distance (GGD).
A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering
In this paper, we propose a persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances.
Comparing Distance Metrics on Vectorized Persistence Summaries
Different vectorizations of PD summary are commonly used in machine learning applications, however distances between vectorized persistence summaries may differ greatly from the distances between the original PDs.
