Diagonal principal component analysis with non greedy l1 norm maximization for face recognition
Diagonal principal component analysis (DiaPCA) is an important method for dimensionality reduction and feature extraction. It usually makes use of the ?2 norm criterion for optimization, and is thus sensitive to outliers. In this paper, we present a DiaPCA with non greedy ?1 norm maximization (DiaPCA L1 non greedy) which is more robust to outliers. Experimental results on two benchmark datasets show the effectiveness and advantages of our proposed method.
Neurocomputing