PCA versus LDA as dimension reduction for individuality of handwriting in writer verification

Ramlee, Rimashadira and Draman @ Muda, Azah Kamilah and Syed Ahmad, Sharifah Sakinah (2014) PCA versus LDA as dimension reduction for individuality of handwriting in writer verification. Journal of Information Assurance and Security, 9 (6). pp. 367-375. ISSN 1554-1010

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Principal component analysis, Dimension reduction, Linear discriminant analysis, Individuality of handwriting, Writer verification
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Muhammad Afiz Ahmad
Date Deposited:	20 Sep 2017 08:55
Last Modified:	16 Aug 2023 15:49
URI:	http://eprints.utem.edu.my/id/eprint/19347
Statistic Details:	View Download Statistic

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