PCA and 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 analys...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/11927/1/ISDA2013_Rima.pdf http://eprints.utem.edu.my/id/eprint/11927/ |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| 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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