Study of the classification in the subspace of the asymmetric principle component analysis
Date of Issue2014
School of Electrical and Electronic Engineering
My dissertation mainly studies the process of principal component analysis method which widely used for pattern classification. Besides, it analyses the problems of principal component analysis method when the training data are unbalance. A new method called asymmetric principal component analysis (APCA) is used to remove the less reliable dimensions to help boost the classification accuracy. When dealing with a two-class classification problem, the discriminant analysis in the APCA subspace is used to adjust the eigenvalues so that we can produce more discriminative and reliable features for the asymmetric classes training data. We have compared this approach with other approaches. The experimental results show the highest accuracy among other approaches. We further find out that the optimal weight factor of different type of training classes have some relationship with the distribution of the training data.
DRNTU::Engineering::Electrical and electronic engineering