Comparison of feature dimension reduction approach for writer verification

Abstract

Dimension reduction is useful approach in data analysis application. In this paper, research is done to test whether the concept of Dimension reduction can be applied to improve writer verification process results. Two approaches have been chosen to be compared which are Features Selection and Feature Transformation, where the comparison is on the way of reducing the dimension of writer handwritten data. Both approaches have slightly difference results in reducing the data and classification accuracy. The objective of this paper is to observe the differences between both approaches according to the classification accuracy results, by using some classification techniques. © Springer Science+Business Media Singapore 2014.