Efficient Weingarten map and curvature estimation on manifolds

In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is established to analyze the asymptotic property of the estimator. In particular, we show the convergence rate as the sample size...

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Published inMachine learning Vol. 110; no. 6; pp. 1319 - 1344
Main Authors Cao, Yueqi, Li, Didong, Sun, Huafei, Assadi, Amir H., Zhang, Shiqiang
Format Journal Article
LanguageEnglish
Published New York Springer US 01.06.2021
Springer Nature B.V
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Abstract In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is established to analyze the asymptotic property of the estimator. In particular, we show the convergence rate as the sample size tends to infinity. We verify the convergence rate through simulated data and apply the estimated Weingarten map to curvature estimation and point cloud simplification to multiple real data sets.
AbstractList In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A statistical model is established to analyze the asymptotic property of the estimator. In particular, we show the convergence rate as the sample size tends to infinity. We verify the convergence rate through simulated data and apply the estimated Weingarten map to curvature estimation and point cloud simplification to multiple real data sets.
Author Li, Didong
Sun, Huafei
Assadi, Amir H.
Zhang, Shiqiang
Cao, Yueqi
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  organization: Department of Computer Science, Princeton University, Department of Biostatistics, University of California Los Angeles
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  givenname: Amir H.
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  organization: Department of Mathematics, University of Wisconsin Madison
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  givenname: Shiqiang
  surname: Zhang
  fullname: Zhang, Shiqiang
  organization: School of Mathematics and Statistics, Beijing Institute of Technology
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Keywords Curvature estimation
Weingarten map
Point cloud data
Unsupervised learning
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Snippet In this paper, we propose an efficient method to estimate the Weingarten map for point cloud data sampled from manifold embedded in Euclidean space. A...
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SubjectTerms Artificial Intelligence
Asymptotic properties
Computer Science
Control
Convergence
Curvature
Euclidean geometry
Euclidean space
Machine Learning
Manifolds
Mechatronics
Natural Language Processing (NLP)
Robotics
Simulation and Modeling
Statistical models
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Title Efficient Weingarten map and curvature estimation on manifolds
URI https://link.springer.com/article/10.1007/s10994-021-05953-4
https://www.proquest.com/docview/2542532534
Volume 110
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