東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linear Subspace and Manifold Learnin...

St. Thomas, Brian.

Linked to FindBook

Google Book

Amazon

博客來

Linear Subspace and Manifold Learning via Extrinsic Geometry.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Linear Subspace and Manifold Learning via Extrinsic Geometry./
Author:	St. Thomas, Brian.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	154 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
Contained By:	Dissertation Abstracts International76-12B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3719241
ISBN:	9781321995428

Linear Subspace and Manifold Learning via Extrinsic Geometry.
St. Thomas, Brian.

Linear Subspace and Manifold Learning via Extrinsic Geometry. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 154 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.

Thesis (Ph.D.)--Duke University, 2015.

In the last few decades, data analysis techniques have had to expand to handle large sets of data with complicated structure. This includes identifying low dimensional structure in high dimensional data, analyzing shape and image data, and learning from or classifying large corpora of text documents. Common Bayesian and Machine Learning techniques rely on using the unique geometry of these data types, however departing from Euclidean geometry can result in both theoretical and practical complications. Bayesian nonparametric approaches can be particularly challenging in these areas.

ISBN: 9781321995428Subjects--Topical Terms:

517247
Statistics.

Linear Subspace and Manifold Learning via Extrinsic Geometry.
LDR:02102nmm a2200313 4500 001 2119853
005 20170622092923.5
008 180830s2015 ||||||||||||||||| ||eng d
020 $a 9781321995428
035 $a (MiAaPQ)AAI3719241
035 $a AAI3719241
040 $a MiAaPQ $c MiAaPQ
100 1 $a St. Thomas, Brian. $3 3281749
245 1 0 $a Linear Subspace and Manifold Learning via Extrinsic Geometry.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 154 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
500 $a Adviser: Sayan Mukherjee.
502 $a Thesis (Ph.D.)--Duke University, 2015.
520 $a In the last few decades, data analysis techniques have had to expand to handle large sets of data with complicated structure. This includes identifying low dimensional structure in high dimensional data, analyzing shape and image data, and learning from or classifying large corpora of text documents. Common Bayesian and Machine Learning techniques rely on using the unique geometry of these data types, however departing from Euclidean geometry can result in both theoretical and practical complications. Bayesian nonparametric approaches can be particularly challenging in these areas.
520 $a This dissertation proposes a novel approach to these challenges by working with convenient embeddings of the manifold valued parameters of interest, commonly making use of an extrinsic distance or measure on the manifold. Carefully selected extrinsic distances are shown to reduce the computational cost and to increase accuracy of inference. The embeddings are also used to yield straight forward derivations for nonparametric techniques. The methods developed are applied to subspace learning in dimension reduction problems, planar shapes, shape constrained regression, and text analysis.
590 $a School code: 0066.
650 4 $a Statistics. $3 517247
650 4 $a Theoretical mathematics. $3 3173530
650 4 $a Applied mathematics. $3 2122814
690 $a 0463
690 $a 0642
690 $a 0364
710 2 $a Duke University. $b Statistical Science. $3 1023903
773 0 $t Dissertation Abstracts International $g 76-12B(E).
790 $a 0066
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3719241