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Geometric Algorithms for Interpretable Manifold Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geometric Algorithms for Interpretable Manifold Learning./
作者:	Koelle, Samson.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	187 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28962819
ISBN:	9798426800793

Geometric Algorithms for Interpretable Manifold Learning.
Koelle, Samson.

Geometric Algorithms for Interpretable Manifold Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 187 p.

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--University of Washington, 2022.

This item must not be sold to any third party vendors.

This thesis proposes several algorithms in the area of interpretable unsupervised learning. Chapters 3 and 4 introduce a sparse convex regression approach for identifying local diffeomorphisms from a dictionary of interpretable functions. In Chapter 3, this algorithm makes use of an embedding learned by a manifold learning algorithm, while in Chapter 4, this algorithm is applied without the use of a precomputed embedding. Chapter 5 then introduces a set of alternative algorithms that avoid issues stemming from sparse regression, characterizes the tangent space version of this algorithm as identifying isometries when available, and gives a two-stage algorithm combining this approach with the computational advantages of the algorithms in Chapters 3 and 4. Finally, Chapter 6 gives an alternate tangent space estimator based on a learned embedding, and uses this as an initial estimator to tackle the related gradient estimation problem. Together, these approaches provide a toolbox of methods for computing and associating gradient information to learn descriptive parameterizations of data manifolds.

ISBN: 9798426800793Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Gradient estimation

Geometric Algorithms for Interpretable Manifold Learning.
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520 $a This thesis proposes several algorithms in the area of interpretable unsupervised learning. Chapters 3 and 4 introduce a sparse convex regression approach for identifying local diffeomorphisms from a dictionary of interpretable functions. In Chapter 3, this algorithm makes use of an embedding learned by a manifold learning algorithm, while in Chapter 4, this algorithm is applied without the use of a precomputed embedding. Chapter 5 then introduces a set of alternative algorithms that avoid issues stemming from sparse regression, characterizes the tangent space version of this algorithm as identifying isometries when available, and gives a two-stage algorithm combining this approach with the computational advantages of the algorithms in Chapters 3 and 4. Finally, Chapter 6 gives an alternate tangent space estimator based on a learned embedding, and uses this as an initial estimator to tackle the related gradient estimation problem. Together, these approaches provide a toolbox of methods for computing and associating gradient information to learn descriptive parameterizations of data manifolds.
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650 4 $a Computer science. $3 523869
650 4 $a Computational chemistry. $3 3350019
653 $a Gradient estimation
653 $a Group lasso
653 $a Manifold learning
653 $a Quantum chemistry
653 $a Shape space
653 $a Tangent space estimation
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