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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| Format: | Thesis |
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2008
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| Online Access: | http://hdl.handle.net/10356/4238 |