Feature dimension reduction for microarray data analysis

In this project, we target to find effective and unsupervised feature reduction tools for gene expression data classification purpose. We have tackled the problem from both feature selection and feature extraction approaches. Three feature reduction algo- rithms, fast entropy ranking, revised locall...

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Bibliographic Details
Main Author:	Shi, Chao
Other Authors:	Chen Lihui
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits
Online Access:	https://hdl.handle.net/10356/3241
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Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-3241
record_format	dspace
spelling	sg-ntu-dr.10356-32412023-07-04T17:26:29Z Feature dimension reduction for microarray data analysis Shi, Chao Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits In this project, we target to find effective and unsupervised feature reduction tools for gene expression data classification purpose. We have tackled the problem from both feature selection and feature extraction approaches. Three feature reduction algo- rithms, fast entropy ranking, revised locally linear embedding, and feature grouping are proposed, analyzed and tested on several microarray datasets. MASTER OF ENGINEERING (EEE) 2008-09-17T09:25:21Z 2008-09-17T09:25:21Z 2005 2005 Thesis Shi, C. (2005). Feature dimension reduction for microarray data analysis. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/3241 10.32657/10356/3241 Nanyang Technological University application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
topic	DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits Shi, Chao Feature dimension reduction for microarray data analysis
description	In this project, we target to find effective and unsupervised feature reduction tools for gene expression data classification purpose. We have tackled the problem from both feature selection and feature extraction approaches. Three feature reduction algo- rithms, fast entropy ranking, revised locally linear embedding, and feature grouping are proposed, analyzed and tested on several microarray datasets.
author2	Chen Lihui
author_facet	Chen Lihui Shi, Chao
format	Theses and Dissertations
author	Shi, Chao
author_sort	Shi, Chao
title	Feature dimension reduction for microarray data analysis
title_short	Feature dimension reduction for microarray data analysis
title_full	Feature dimension reduction for microarray data analysis
title_fullStr	Feature dimension reduction for microarray data analysis
title_full_unstemmed	Feature dimension reduction for microarray data analysis
title_sort	feature dimension reduction for microarray data analysis
publishDate	2008
url	https://hdl.handle.net/10356/3241
_version_	1772829026365210624

Feature dimension reduction for microarray data analysis

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