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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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 |
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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 |