Abstract
Neighborhood Preserving Embedding (NPE) is a subspace learning algorithm. Since NPE is a linear approximation to Locally Linear Embedding (LLE) algorithm, it has good neighborhood-preserving properties. Although NPE has been applied in many fields, it has limitations to solve recognition task. In this paper, a novel subspace method, named Kernel Fisher Neighborhood Preserving Embedding (KFNPE), is proposed. In this method, discriminant information as well as the intrinsic geometry relations of the local neighborhoods are preserved according to prior class-label information. Moreover, complex nonlinear variations of real face images are represented by nonlinear kernel mapping. Experimental results on ORL face database demonstrate the effectiveness of the proposed method.
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Wang, G., Ou, Z., Ou, F., Liu, D., Han, F. (2007). Kernel Fisher NPE for Face Recognition. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_88
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DOI: https://doi.org/10.1007/978-3-540-72393-6_88
Publisher Name: Springer, Berlin, Heidelberg
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