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PAMI
2007

Feature Subset Selection and Ranking for Data Dimensionality Reduction

8 years 11 months ago
Feature Subset Selection and Ranking for Data Dimensionality Reduction
—A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation.
Hua-Liang Wei, Stephen A. Billings
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where PAMI
Authors Hua-Liang Wei, Stephen A. Billings
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