Abstract
Subspace learning is one of the main directions for face recognition. In this paper, a novel unsupervised subspace learning method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The central idea is to modify the classical locally linear embedding by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Experimental results on Yale face database and FERET face database show the effectiveness of the proposed method....
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Pang, Y., Yu, N., Li, H., Zhang, R., Liu, Z. (2005). Face Recognition Using Neighborhood Preserving Projections. In: Ho, YS., Kim, HJ. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11582267_74
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DOI: https://doi.org/10.1007/11582267_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30040-3
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