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...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 29(2007), 1 vom: 16. Jan., Seite 162-6 |
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| Format: | Aufsatz |
| Sprache: | English |
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2007
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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 |
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| Beschreibung: | Date Completed 30.01.2007 Date Revised 10.11.2019 published: Print Citation Status MEDLINE |
| ISSN: | 1939-3539 |