Journal of Data Science

Yang et al. (2004) developed the two-dimensional principal component analysis (2DPCA) for image representation and recognition, widely used in different fields, including face recognition, biometrics recognition, cancer diagnosis, tumor classification, and others. 2DPCA has been proven to perform better and computationally more efficiently than traditional principal component analysis (PCA). However, some theoretical properties of 2DPCA are still unknown, including determining the number of principal components (PCs) in the training set, which is the critical step in applying 2DPCA. Without rigorous criteria for determining the number of PCs hampers the generalization of the application of 2DPCA. Given this issue, we propose a new method based on parallel analysis to determine the number of PCs in 2DPCA with statistical justification. Several image classification experiments demonstrate that the proposed method compares favourably to other state-of-the-art approaches regarding recognition accuracy and storage requirement, with a low computational cost.