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Asymptotic Analysis of Locally Linea...
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Wu, Nan.
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Asymptotic Analysis of Locally Linear Embedding.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Asymptotic Analysis of Locally Linear Embedding./
作者:
Wu, Nan.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
120 p.
附註:
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:
Dissertation Abstracts International79-12B(E).
標題:
Mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10790943
ISBN:
9780438190443
Asymptotic Analysis of Locally Linear Embedding.
Wu, Nan.
Asymptotic Analysis of Locally Linear Embedding.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 120 p.
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Thesis (Ph.D.)--University of Toronto (Canada), 2018.
Since its introduction in 2000, locally linear embedding (LLE) algorithm has been widely applied in data science. In this thesis, we provide an asymptotical analysis of LLE under the manifold setup. First, by study the regularized barycentric problem, we derive the corresponding kernel function of LLE. Second, we show that when the point cloud is sampled from a general closed manifold, asymptotically LLE algorithm does not always recover the Laplace-Beltrami operator, and the result may depend on the non-uniform sampling. We demonstrate that a careful choosing of the regularization is necessary to ensure the recovery of the Laplace-Beltrami operator. A comparison with the other commonly applied nonlinear algorithms, particularly the diffusion map, is provided. Moreover, we discuss the relationship between two common nearest neighbor search schemes and the relationship of LLE with the locally linear regression. At last, we consider the case when the point cloud is sampled from a manifold with boundary. We show that if the regularization is chosen correctly, LLE algorithm asymptotically recovers a linear second order differential operator with ``free'' boundary condition. Such operator coincides with Laplace-Beltrami operator in the interior of the manifold. We further modify LLE algorithm to the Dirichlet Graph Laplacian algorithm which can be used to recover the Laplace-Beltrami operator of the manifold with Dirichlet boundary condition.
ISBN: 9780438190443Subjects--Topical Terms:
515831
Mathematics.
Asymptotic Analysis of Locally Linear Embedding.
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Since its introduction in 2000, locally linear embedding (LLE) algorithm has been widely applied in data science. In this thesis, we provide an asymptotical analysis of LLE under the manifold setup. First, by study the regularized barycentric problem, we derive the corresponding kernel function of LLE. Second, we show that when the point cloud is sampled from a general closed manifold, asymptotically LLE algorithm does not always recover the Laplace-Beltrami operator, and the result may depend on the non-uniform sampling. We demonstrate that a careful choosing of the regularization is necessary to ensure the recovery of the Laplace-Beltrami operator. A comparison with the other commonly applied nonlinear algorithms, particularly the diffusion map, is provided. Moreover, we discuss the relationship between two common nearest neighbor search schemes and the relationship of LLE with the locally linear regression. At last, we consider the case when the point cloud is sampled from a manifold with boundary. We show that if the regularization is chosen correctly, LLE algorithm asymptotically recovers a linear second order differential operator with ``free'' boundary condition. Such operator coincides with Laplace-Beltrami operator in the interior of the manifold. We further modify LLE algorithm to the Dirichlet Graph Laplacian algorithm which can be used to recover the Laplace-Beltrami operator of the manifold with Dirichlet boundary condition.
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