Robust classifiers for data reduced via random projections
The computational cost for most classification algorithms is dependent on the dimensionality of the input samples. As the dimensionality could be high in many cases, particularly those associated with image classification, reducing the dimensionality of the data becomes a necessity. The traditional...
| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society. - 1996. - 40(2010), 5 vom: 15. Okt., Seite 1359-71 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2010
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | The computational cost for most classification algorithms is dependent on the dimensionality of the input samples. As the dimensionality could be high in many cases, particularly those associated with image classification, reducing the dimensionality of the data becomes a necessity. The traditional dimensionality reduction methods are data dependent, which poses certain practical problems. Random projection (RP) is an alternative dimensionality reduction method that is data independent and bypasses these problems. The nearest neighbor classifier has been used with the RP method in classification problems. To obtain higher recognition accuracy, this study looks at the robustness of RP dimensionality reduction for several recently proposed classifiers--sparse classifier (SC), group SC (along with their fast versions), and the nearest subspace classifier. Theoretical proofs are offered regarding the robustness of these classifiers to RP. The theoretical results are confirmed by experimental evaluations |
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| Beschreibung: | Date Completed 25.01.2011 Date Revised 15.09.2010 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1941-0492 |
| DOI: | 10.1109/TSMCB.2009.2038493 |