Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
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DOI：
10.1007/11538059_13
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发表时间：
2005-08
期刊：
影响因子：
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通讯作者：
Yanwei Pang;Lei Zhang;Zhengkai Liu;Nenghai Yu;Houqiang Li
中科院分区：
文献类型：
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作者：
Yanwei Pang;Lei Zhang;Zhengkai Liu;Nenghai Yu;Houqiang Li
降维是模式识别和信息检索任务中克服维数诅咒的关键步骤。本文提出了一种新的无监督线性降维方法——邻域保持投影（NPP）。与传统的主成分分析（PCA）等线性降维方法相比，该方法具有良好的邻域保持特性。NPP的主要思想是通过引入线性变换矩阵来近似经典的局部线性嵌入（LLE）。变换矩阵是通过对某一目标函数进行优化得到的。在已知流形数据上的初步实验结果表明了该方法的有效性。
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.