Abstract
Locally Linear Embedding (LLE) is a popular dimension reduction technique due to its nonlinearity property. However, LLE is restricted to its unsupervised nature and “out-of-sample problem” in which suboptimal to face recognition problem. Hence, we propose a supervised and linear approximation of LLE, known as Neighborhood Preserving Discriminant Embedding (NPDE). Using the class information, NPDE finds an optimal projection so that the ratio of the within-neighborhood scatter and the between-neighborhood scatter is minimized. NPDE signifies the local neighboring geometry that corresponding to the nonlinear underlying data structure in the image space. Based on this intuition, NPDE shows better discriminative capability in face recognition.
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