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231223s2004 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2004.13
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|a DE-627
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|a eng
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|a Loog, Marco
|e verfasserin
|4 aut
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|a Linear dimensionality reduction via a heteroscedastic extension of LDA
|b the Chernoff criterion
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|c 2004
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|a Text
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|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 31.07.2008
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|a Date Revised 26.06.2008
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|a published: Print
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|a Citation Status MEDLINE
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|a We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic two-class technique which utilizes the so-called Chernoff criterion, and successfully extends the well-known linear discriminant analysis (LDA). The latter, which is based on the Fisher criterion, is incapable of dealing with heteroscedastic data in a proper way. For the two-class case, the between-class scatter is generalized so to capture differences in (co)variances. It is shown that the classical notion of between-class scatter can be associated with Euclidean distances between class means. From this viewpoint, the between-class scatter is generalized by employing the Chernoff distance measure, leading to our proposed heteroscedastic measure. Finally, using the results from the two-class case, a multiclass extension of the Chernoff criterion is proposed. This criterion combines separation information present in the class mean as well as the class covariance matrices. Extensive experiments and a comparison with similar dimension reduction techniques are presented
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|a Journal Article
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|a Duin, Robert P W
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 26(2004), 6 vom: 26. Juni, Seite 732-9
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:26
|g year:2004
|g number:6
|g day:26
|g month:06
|g pages:732-9
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|u http://dx.doi.org/10.1109/TPAMI.2004.13
|3 Volltext
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|d 26
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|h 732-9
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