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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.
Dimensionality reduction, feature selection, high-dimensional data.
Stephen A. Billings, Hua-Liang Wei, "Feature Subset Selection and Ranking for Data Dimensionality Reduction", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 162-166, January 2007, doi:10.1109/TPAMI.2007.11
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