Unsupervised feature selection based on principal components analysis

An important issue related to mining large data sets, both in dimension and size, is of selecting a subset of the original features. In this thesis, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The algorithm consists of two steps—...

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Main Author:	Fang, Ji
Other Authors:	Mao, Kezhi
Format:	Theses and Dissertations
Published:	2008
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	http://hdl.handle.net/10356/4238
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Institution:	Nanyang Technological University

id	sg-ntu-dr.10356-4238
record_format	dspace
spelling	sg-ntu-dr.10356-42382023-07-04T15:09:13Z Unsupervised feature selection based on principal components analysis Fang, Ji Mao, Kezhi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems An important issue related to mining large data sets, both in dimension and size, is of selecting a subset of the original features. In this thesis, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The algorithm consists of two steps— Pre-selection and selection. Pre-selection is based on Procrustes Analysis, which keeps the original characters as many as possible. The second step is based on feature similarity measure, with the aim of reducing the feature redundancy. Master of Science (Computer Control and Automation) 2008-09-17T09:47:26Z 2008-09-17T09:47:26Z 2004 2004 Thesis http://hdl.handle.net/10356/4238 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::Computer hardware, software and systems
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Fang, Ji Unsupervised feature selection based on principal components analysis
description	An important issue related to mining large data sets, both in dimension and size, is of selecting a subset of the original features. In this thesis, we describe an unsupervised feature selection algorithm suitable for data sets, large in both dimension and size. The algorithm consists of two steps— Pre-selection and selection. Pre-selection is based on Procrustes Analysis, which keeps the original characters as many as possible. The second step is based on feature similarity measure, with the aim of reducing the feature redundancy.
author2	Mao, Kezhi
author_facet	Mao, Kezhi Fang, Ji
format	Theses and Dissertations
author	Fang, Ji
author_sort	Fang, Ji
title	Unsupervised feature selection based on principal components analysis
title_short	Unsupervised feature selection based on principal components analysis
title_full	Unsupervised feature selection based on principal components analysis
title_fullStr	Unsupervised feature selection based on principal components analysis
title_full_unstemmed	Unsupervised feature selection based on principal components analysis
title_sort	unsupervised feature selection based on principal components analysis
publishDate	2008
url	http://hdl.handle.net/10356/4238
_version_	1772825593050562560

Unsupervised feature selection based on principal components analysis

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