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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
verfasst von : Yanwei Pang, Lei Zhang, Zhengkai Liu, Nenghai Yu, Houqiang Li
Erschienen in: Advances in Intelligent Computing
Verlag: Springer Berlin Heidelberg
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Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction 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 main idea of NPP is to approximate the classical locally linear embedding (i.e. LLE) by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Preliminary experimental results on known manifold data show the effectiveness of the proposed method.