Abstract Two-dimensional neighborhood preserving discriminant embedding (2DNPDE) is proposed in this paper. 2DNPDE is supervised feature extraction algorithm based on 2D image matrices. For representing the within-class neighborhood structure and the between-class distance relationship of samples, the within-class affinity matrix and the between-class similarity matrix are constructed respectively. The projection space obtained by 2DNPDE not only makes the low dimensional embedding of data points from different classes far from each other, but also preserves the neighborhood structure of samples from the same class and the distance relationship of samples from the different classes. The experimental results on the ORL and AR face databases show that the proposed algorithm has better recognition performance.
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2015, Vol. 28 
