Fréchet Means for Distributions of Persistence Diagrams
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The L2 Wasserstein metric on B∞ results in a metric space that is simpler to work with computationally, and all the existence results from Mileyko et al. (2011) are also shown to hold in Turner et al. (2014).
“The resulting metric space is simpler to work with computationally, and all the existence results from Mileyko et al. (2011) are also shown to hold in Turner et al. (2014).”
From Topological pattern recognition for point cloud data (2014) · cites this paper as Turner et al. (2014)
Turner, Mileyko, Mukherjee and Harer (2014) made the existence theorem algorithmic, but for a somewhat different choice of metric and metric space.
“Turner, Mileyko, Mukherjee and Harer (2014) made this existence theorem algorithmic, but for a somewhat different choice of metric and metric space.”
From Topological pattern recognition for point cloud data (2014) · cites this paper as Turner, Mileyko, Mukherjee and Harer (2014)
Cites
Metric Spaces of Non-Positive Curvature
Bridson, M. R., & Haefliger, A. (1999) · Grundlehren der mathematischen Wissenschaften · doi:10.1007/978-3-662-12494-9 · Cited by 4,714 · see citing works
Algorithms for the Assignment and Transportation Problems
Munkres, J. R. (1957) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0105003 · Cited by 4,001 · see citing works
Penrose, M. (2003) · doi:10.1093/acprof:oso/9780198506263.001.0001 · Cited by 2,480 · see citing works
Computational topology an introduction
Edelsbrunner, H., & Harer, J. (2010) · Cited by 1,718 · see citing works
Self-citationRicci curvature for metric-measure spaces via optimal transport
Lott, J. A., & Villani, C. (2009) · Annals of Mathematics · doi:10.4007/annals.2009.169.903 · Cited by 1,281 · see citing works
Molchanov, I. S. (2017) · Probability theory and stochastic modelling · doi:10.1007/978-1-4471-7349-6 · Cited by 626 · see citing works
Finding the homology of submanifolds with high confidence from random samples
Niyogi, P., Smale, S., & Weinberger, S. (2008) · Discrete & Computational Geometry · doi:10.1007/s00454-008-9053-2 · Cited by 523 · see citing works
A.D. Alexandrov spaces with curvature bounded below
Burago, Y. D., Gromov, M. L., & Perel'man, G. Y. (1992) · Russian Mathematical Surveys · doi:10.1070/rm1992v047n02abeh000877 · Cited by 413 · see citing works
Lipschitz Functions Have L p -Stable Persistence
Cohen-Steiner, D., Edelsbrunner, H., Harer, J., & Mileyko, Y. (2010) · Foundations of Computational Mathematics · doi:10.1007/s10208-010-9060-6 · Cited by 284 · see citing works
Self-citationProbability measures on the space of persistence diagrams
Mileyko, Y., Mukherjee, S., & Harer, J. (2011) · Inverse Problems · doi:10.1088/0266-5611/27/12/124007 · Cited by 249 · see citing works
Self-citationCentral Limit Theorems for Some Graphs in Computational Geometry
Penrose, M. D., & Yukich, J. E. (2001) · The Annals of Applied Probability · doi:10.1214/aoap/1015345393 · Cited by 192 · see citing works
Topology of random clique complexes
Kahle, M. (2008) · Discrete Mathematics · doi:10.1016/j.disc.2008.02.037 · Cited by 190 · see citing works
A Topological View of Unsupervised Learning from Noisy Data
Niyogi, P., Smale, S. T., & Weinberger, S. (2011) · SIAM Journal on Computing · doi:10.1137/090762932 · Cited by 150 · see citing works
Kahle, M. (2011) · Discrete & Computational Geometry · doi:10.1007/s00454-010-9319-3 · Cited by 150 · see citing works
Semiconcave Functions in Alexandrov’s Geometry
Petrunin, A. (2006) · Surveys in Differential Geometry · doi:10.4310/sdg.2006.v11.n1.a6 · Cited by 139 · see citing works
A Sampling Theory for Compact Sets in Euclidean Space
Chazal, F., Cohen‐Steiner, D., & Lieutier, A. (2009) · Discrete & Computational Geometry · doi:10.1007/s00454-009-9144-8 · Cited by 132 · see citing works
Limit theorems for Betti numbers of random simplicial complexes
Kahle, M., & Meckes, E. (2013) · Homology Homotopy and Applications · doi:10.4310/hha.2013.v15.n1.a17 · Cited by 132 · see citing works
Heat Kernels and Analysis on Manifolds, Graphs, and Metric Spaces
Auscher, P., Coulhon, T., & Grigor’yan, A. (2003) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/338 · Cited by 110 · see citing works
Persistent homology for random fields and complexes
Adler, R. J., Bobrowski, O., Borman, M. S., Subag, E., & Weinberger, S. (2010) · Institute of Mathematical Statistics Collections · doi:10.1214/10-imscoll609 · Cited by 91 · see citing works
Open map theorem for metric spaces
Lytchak, A. (2006) · St Petersburg Mathematical Journal · doi:10.1090/s1061-0022-06-00916-2 · Cited by 59 · see citing works
Statistical topology via Morse theory persistence and nonparametric estimation
Bubenik, P., Carlsson, G., Kim, P. T., & Luo, Z. (2010) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/516/10167 · Cited by 44 · see citing works
Barycenters in Alexandrov spaces of curvature bounded below
Ohta, S. (2012) · Advances in Geometry · doi:10.1515/advgeom-2011-058 · Cited by 39 · see citing works
Local Homology Transfer and Stratification Learning
Bendich, P. L., Wang, B., & Mukherjee, S. (2012) · ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611973099.107 · Cited by 29 · see citing works
Self-citationThe centenary of of the re-interment of John Hunter.
GORDON-TAYLOR, G. (1959) · PubMed Central · doi:10.5555/2095116.2095223 · Cited by 24 · see citing works
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A roadmap for the computation of persistent homology
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Statistical topological data analysis using persistence landscapesPreprint
Bubenik, P. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1207.6437 · Cited by 628 · see citing works
Persistence Images: A Stable Vector Representation of Persistent HomologyPreprint
Adams, H., Chepushtanova, S., Emerson, T., Hanson, E., Kirby, M. J. L., Motta, F. C., Neville, R., Peterson, C., Shipman, P. D., & Ziegelmeier, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06217 · Cited by 517 · see citing works
Confidence sets for persistence diagrams
Fasy, B. T., Lecci, F., Rinaldo, A., Wasserman, L., Balakrishnan, S., & Singh, A. (2014) · The Annals of Statistics · doi:10.1214/14-aos1252 · Cited by 251 · see citing works
Topological pattern recognition for point cloud data
Carlsson, G. (2014) · Acta Numerica · doi:10.1017/s0962492914000051 · Cited by 230 · see citing works
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A persistence landscapes toolbox for topological statistics
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Munch, E. (2017) · Journal of Learning Analytics · doi:10.18608/jla.2017.42.6 · Cited by 163 · see citing works
Persistent homology transform for modeling shapes and surfaces
Turner, K., Mukherjee, S., & Boyer, D. (2014) · Information and Inference A Journal of the IMA · doi:10.1093/imaiai/iau011 · Cited by 155 · see citing works
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Persistent-homology-based machine learning: a survey and a comparative study
Pun, C. S., Lee, S. X., & Xia, K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10146-z · Cited by 223 · see citing works
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Obayashi, I., Hiraoka, Y., & Kimura, M. (2018) · Journal of Applied and Computational Topology · doi:10.1007/s41468-018-0013-5 · Cited by 123 · see citing works
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Kovacev-Nikolic, V., Bubenik, P., Nikolić, D., & Heo, G. (2016) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2015-0057 · Cited by 116 · see citing works
Crawford, L. A., Monod, A., Chen, A. X., Mukherjee, S., & Rabadán, R. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1671198 · Cited by 91 · see citing works
Self-citationTopological data analysis of single-trial electroencephalographic signals
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Pun, C. S., Xia, K., & Lee, S. X. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3275996 · Cited by 89 · see citing works
The shape of things to come: Topological data analysis and biology, from molecules to organisms
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Yan, L., Masood, T. B., Sridharamurthy, R., Rasheed, F., Natarajan, V., Hotz, I., & Wang, B. (2021) · Computer Graphics Forum · doi:10.1111/cgf.14331 · Cited by 81 · see citing works
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Phillips, J. M., Wang, B., & Zheng, Y. L. (2013) · arXiv (Cornell University) · doi:10.4230/lipics.socg.2015.857 · Cited by 41 · see citing works
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Ramamurthy, K. N., Varshney, K. R., & Thiagarajan, J. J. (2014) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp.2014.6884586 · Cited by 9 · see citing works
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Mike, J. L., & Maroulas, V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.02739 · Cited by 9 · see citing works
Topology Data Analysis Using Mean Persistence Landscapes in Financial Crashes
Aguilar, A., & Ensor, K. B. (2020) · Journal of Mathematical Finance · doi:10.4236/jmf.2020.104038 · Cited by 8 · see citing works
k-means clustering for persistent homology
Cao, Y., Leung, P., & Monod, A. (2024) · Advances in Data Analysis and Classification · doi:10.1007/s11634-023-00578-y · Cited by 8 · see citing works
Optimization of Spectral Wavelets for Persistence-Based Graph Classification
Yim, K. M., & Leygonie, J. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2021.651467 · Cited by 8 · see citing works
An Overview of the Topology ToolKit
Falk, M., Favelier, G., Gueunet, C., Guillou, P., Kamakshidasan, A., Klacansky, P., Levine, J. A., Lukasczyk, J., Sakurai, D., Soler, M., et al. (2019) · HAL · Cited by 8 · see citing works
A random persistence diagram generator
Papamarkou, T., Nasrin, F., Lawson, A., Gong, N., Rios, O. R., & Maroulas, V. (2022) · Statistics and Computing · doi:10.1007/s11222-022-10141-y · Cited by 8 · see citing works
Convergence of persistence diagram in the sparse regime
Owada, T. (2022) · The Annals of Applied Probability · doi:10.1214/22-aap1800 · Cited by 8 · see citing works
Topological data analysis reveals differences between simulated galaxies and dark matter haloes
Ouellette, A., Holder, G. P., & Kerman, E. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1765 · Cited by 8 · see citing works
Crawford, L. A., Monod, A., Chen, A., Mukherjee, S., & Rabadán, R. (2016) · arXiv (Cornell University) · Cited by 8 · see citing works
Self-citationMurayama, B., Kobayashi, M., Aoki, M., Ishibashi, S., Saito, T., Nakamura, T., Teramoto, H., & Taketsugu, T. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01204 · Cited by 7 · see citing works
Association-Free Direct Filtering of Multi-Target Random Finite Sets with Set Distance Measures
Hanebeck, U. D., & Baum, M. (2015) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000051028 · Cited by 7 · see citing works
- Authors
- Turner, K., Mileyko, Y., Mukherjee, S., & Harer, J.
- Affiliations
- University of Chicago (US)
- University of Hawaiʻi at Mānoa (US)
- Duke University (US)
- Corresponding author
- Turner, Katharine
- Venue
- Discrete & Computational Geometry
- Published
- 2014-07
- Volume
- 52
- Issue
- 1
- Pages
- 44-70
- ISSN
- 0179-5376 (ISSN-L)
- 1432-0444
- OpenAlex
- W2047158247
- Cited by
- 207
- References
- 25
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 20 · 2023: 29 · 2022: 20 · 2021: 30 · 2020: 25 · 2019: 16 · 2018: 14 · 2017: 8 · 2016: 10 · 2015: 11 · 2014: 6 · 2013: 4 · 2012: 1
- FWCI
- 12.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Discrete & Computational Geometry (publishedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Funders
- National Science Foundation (1049290)
- National Institutes of Health
- Defense Advanced Research Projects Agency
- Defense Threat Reduction Agency (HDTRA1)
- Air Force Office of Scientific Research (FA9550-, FA9550-10-1-, FA9550‐10‐1‐0436)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Mathematics › Applied Mathematics › Geometric Analysis and Curvature Flows
- Physical Sciences › Mathematics › Applied Mathematics › Point processes and geometric inequalities
- Keywords
- Fréchet mean, persistence diagrams, persistence landscapes, topological data analysis, law of large numbers, Gaussian random fields, local minima
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