Fusion of effective dimension reduction and discriminative dictionary learning for high-dimensional classification

Abstract

High-dimensional classification is a hot topic of pattern recognition. However, its performance has much room for improvement due to environment uncertainties and other factors. For example, redundant information from high dimensional data, illumination, and occlusion may lead to the inferior classification performance. Additionally, some potentially useful features of high-dimensional data are not fully captured. To address these problems, we propose a fusion of effective dimension reduction and discriminative dictionary learning for high-dimensional classification. A novel dimension reduction approach is employed to map training samples into a low-dimensional space by using the distance and label information. Then in the low-dimensional subspace, we integrate atomic Fisher constraint and local constraint of coding coefficients into the dictionary learning. Thus the discriminative atoms are effectively extracted. Finally, experimental results prove the efficacy of the proposed model. Besides, the comparative experiments with AlexNet and VGG19 prove the superiority of our method over some deep learning approaches.