• Corpus ID: 57189419

Approximate Nearest Neighbors in the Space of Persistence Diagrams

@article{Fasy2018ApproximateNN,
title={Approximate Nearest Neighbors in the Space of Persistence Diagrams},
author={Brittany Terese Fasy and Xiaozhou He and Zhihui Liu and Samuel Micka and David L. Millman and Binhai Zhu},
journal={ArXiv},
year={2018},
volume={abs/1812.11257}
}
Persistence diagrams are important tools in the field of topological data analysis that describe the presence and magnitude of features in a filtered topological space. However, current approaches for comparing a persistence diagram to a set of other persistence diagrams is linear in the number of diagrams or do not offer performance guarantees. In this paper, we apply concepts from locality-sensitive hashing to support approximate nearest neighbor search in the space of persistence diagrams…
7 Citations
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• Computer Science, Mathematics
SoCG
• 2021
For approximating the bottleneck distance, sketches can also be used to compute a linear-size neighborhood graph directly, obviating the need for geometric data structures used in state-of-the-art methods for bottleneck computation.
On Computing a Center Persistence Diagram
It is shown, by a non-trivial reduction from the Planar 3D-Matching problem, that this problem is NP-hard even when $m=3$ diagrams are given, which implies that the general center problem for persistence diagrams under the bottleneck distance, when $P_i$'s possibly have different sizes, is also NP- hard.
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ArXiv
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The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports $8-approximate nearest bottleneck queries in$O(-\lg(d_B(D,Q)) n \lg^3 n)$time, which is a simple time algorithm to support nearest bottleneck distance queries for any two point sets P and Q. Computational Geometry Column 70: Processing Persistence Diagrams as Purely Geometric Objects In this column, we review the most recent results on processing persistence diagrams as purely geometric objects. (Yes, this is not a typo! While persistence diagrams originally come from Computational Geometry Column 70 • B. Zhu • Computer Science SIGACT News • 2020 In this column, the most recent results on processing persistence diagrams as purely geometric objects are reviewed, and the approximate nearest neighbor query under the bottleneck distance is reviewed. A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering • Computer Science, Medicine IEEE Transactions on Visualization and Computer Graphics • 2022 A persistence diagram hashing framework that learns a binary code representation of persistence diagrams, which allows for fast computation of distances, and demonstrates that the method is significantly faster with the potential of less memory usage, while retaining comparable or better quality comparisons. References SHOWING 1-10 OF 31 REFERENCES Locality-Sensitive Hashing of Curves • Computer Science, Mathematics SoCG • 2017 The first locality-sensitive hashing schemes for these distance measures using the discrete Frechet distance or the dynamic time warping distance are devised, which provide a trade-off between approximation quality and computational performance. Geometry Helps to Compare Persistence Diagrams • Computer Science ACM J. Exp. Algorithmics • 2017 This work implements geometric variants of the Hopcroft-Karp algorithm for bottleneck matching and of the auction algorithm by Bertsekas for Wasserstein distance computation that lead to a substantial performance gain over their purely combinatorial counterparts. Learning Simplicial Complexes from Persistence Diagrams This paper presents an algorithm for reconstructing plane graphs K=(V,E) in R^2, i.e., a planar graph with vertices in general position and a straight-line embedding, from a quadratic number height filtrations and their respective persistence diagrams. Approximate nearest neighbor algorithms for Frechet distance via product metrics • P. Indyk • Computer Science, Mathematics SCG '02 • 2002 Several data structures using space (quasi)-polynomial in n and d, and query time sublinear in n, have been discovered for approximate NNS under l1 and l2 [14, 12, 11] and l1 [10] norms. Persistent Homology Transform for Modeling Shapes and Surfaces • Mathematics • 2013 In this paper we introduce a statistic, the persistent homology transform (PHT), to model surfaces in$\mathbb{R}^3$and shapes in$\mathbb{R}^2\$. This statistic is a collection of persistence
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