出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The aim of this paper is to present a comparative study of two linear dimension reductionmethods namely PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis).The main idea of PCA is to transform the high dimensional input space onto the feature spacewhere the maximal variance is displayed. The feature selection in traditional LDA is obtainedby maximizing the difference between classes and minimizing the distance within classes. PCAfinds the axes with maximum variance for the whole data set where LDA tries to find the axesfor best class seperability. The proposed method is experimented over a general image databaseusing Matlab. The performance of these systems has been evaluated by Precision and Recallmeasures. Experimental results show that PCA based dimension reduction method gives thebetter performance in terms of higher precision and recall values with lesser computationalcomplexity than the LDA based method.
关键词：Color histogram; Feature Extraction; Euclidean distance; Principal Component Analysis;Linear Discriminant Analysis; Eigen Values; Eigen Vectors.
