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

Learning methods in reproducing kern...

Yang, Hojin.

Linked to FindBook

Google Book

Amazon

博客來

Learning methods in reproducing kernel Hilbert space based on high-dimensional features.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning methods in reproducing kernel Hilbert space based on high-dimensional features./
Author:	Yang, Hojin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	156 p.
Notes:	Source: Dissertations Abstracts International, Volume: 78-01, Section: B.
Contained By:	Dissertations Abstracts International78-01B.
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10119759
ISBN:	9781339809502

Learning methods in reproducing kernel Hilbert space based on high-dimensional features.
Yang, Hojin.

Learning methods in reproducing kernel Hilbert space based on high-dimensional features. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 156 p.

Source: Dissertations Abstracts International, Volume: 78-01, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2016.

This item is not available from ProQuest Dissertations & Theses.

The first topic focuses on the dimension reduction method via the regularization. We propose the selection for principle components via LASSO. This method assumes that some unknown latent variables are related to the response under the highly correlate covariate structure. L1 regularization plays a key role in adaptively finding a few liner combinations in contrast to the persistent idea that is to employ a few leading principal components. The consistency of regression coefficients and selected model are asymptotically proved and numerical performances are shown to support our suggestion. The proposed method is applied to analyze microarray data and cancer data. Second and third topics focus on the approaches of the independent screening and the dimension reduction with the machine learning approach using positive definite kernels. A Key ingredient matter of these papers is to use reproducing kernel Hilbert space (RKHS) theory. Specifically, we proposed Multiple Projection Model (MPM) and Single Index Latent Factor Model (SILFM) to build an accurate prediction model for clinical outcomes based on a massive number of features. MPM and SILFM can be summarized as three-stage estimation, screening, dimension reduction, and nonlinear fitting. Screening and dimension reduction are unique approaches of two novel methods. The convergence property of the proposed screening method and the risk bound for SILFM are systematically investigated. The results from several simulation scenarios are shown to support it. The proposed method is applied to analyze brain image data and its clinical behavior response.

ISBN: 9781339809502Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Dimension reduction

Learning methods in reproducing kernel Hilbert space based on high-dimensional features.
LDR:03000nmm a2200397 4500 001 2198399
005 20200810095939.5
008 200831s2016 ||||||||||||||||| ||eng d
020 $a 9781339809502
035 $a (MiAaPQ)AAI10119759
035 $a (MiAaPQ)unc:15850
035 $a AAI10119759
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yang, Hojin. $3 3423848
245 1 0 $a Learning methods in reproducing kernel Hilbert space based on high-dimensional features.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 156 p.
500 $a Source: Dissertations Abstracts International, Volume: 78-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Ibrahim, Joseph G.;Zhu, Hongtu.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2016.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a The first topic focuses on the dimension reduction method via the regularization. We propose the selection for principle components via LASSO. This method assumes that some unknown latent variables are related to the response under the highly correlate covariate structure. L1 regularization plays a key role in adaptively finding a few liner combinations in contrast to the persistent idea that is to employ a few leading principal components. The consistency of regression coefficients and selected model are asymptotically proved and numerical performances are shown to support our suggestion. The proposed method is applied to analyze microarray data and cancer data. Second and third topics focus on the approaches of the independent screening and the dimension reduction with the machine learning approach using positive definite kernels. A Key ingredient matter of these papers is to use reproducing kernel Hilbert space (RKHS) theory. Specifically, we proposed Multiple Projection Model (MPM) and Single Index Latent Factor Model (SILFM) to build an accurate prediction model for clinical outcomes based on a massive number of features. MPM and SILFM can be summarized as three-stage estimation, screening, dimension reduction, and nonlinear fitting. Screening and dimension reduction are unique approaches of two novel methods. The convergence property of the proposed screening method and the risk bound for SILFM are systematically investigated. The results from several simulation scenarios are shown to support it. The proposed method is applied to analyze brain image data and its clinical behavior response.
590 $a School code: 0153.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
653 $a Dimension reduction
653 $a Independent screening
653 $a Kernel ridge regression
653 $a Latent factor model
653 $a Prediction
690 $a 0308
690 $a 0463
710 2 $a The University of North Carolina at Chapel Hill. $b Biostatistics. $3 1023527
773 0 $t Dissertations Abstracts International $g 78-01B.
790 $a 0153
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10119759