PCA versus LDA as dimension reduction for individuality of handwriting in writer verification
Principal Component Analysis and Linear Discriminant Analysis are the most popular approach used in statistical data analysis. Both of these approaches are usually implemented as traditional linear technique for Dimension reduction approach. Dimension reduction is useful approach in data analysis ap...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en |
| Published: |
MIR Labs
2014
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/19347/2/JIAS_Azah2014.pdf http://eprints.utem.edu.my/id/eprint/19347/ http://www.mirlabs.org/jias/secured/Volume9-Issue6/Paper38.pdf |
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| Summary: | Principal Component Analysis and Linear Discriminant Analysis are the most popular approach used in statistical data analysis. Both of these approaches are usually implemented as traditional linear technique for Dimension reduction approach. Dimension reduction is useful approach in data analysis application. The concept of dimension reduction will help the process of identifying the most important features in handwritten data which also called as individuality of the handwriting. Where, this individuality will help the verification process in order to verify the handwritten document. The
purposed of this paper is to perform both techniques above in
writer verification process in order to acquire the individuality
of the handwriting. Classification process will be use to evaluate
the effectiveness of both approach performance in form of classification accuracy. |
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