仿真实验结果表明，与基于非负矩阵分解(NMF)、局部非负矩阵分解(LNMF)和Hoyer-NNSC的掌纹识别方法相比，该算法在掌纹识别研究中有较高的可行性和实用性。
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局部保持非负矩阵分解 Locality Presering NMF
非负矩阵分解具有非负性和局部性的特点，是一种新型的特征提取方法。
Non-negative matrix factorization has non-negative and local characteristics, and it is a new feature extraction method.
非负矩阵分解过程中，适当地选取特征空间的维数能够获得原始数据的局部特征。
The local feature based representation could be obtained by choosing suitable dimension of the feature subspace in NMF.
摘要：非负矩阵分解方法是基于局部特征的特征提取方法，已经成功用于人脸识别。
Absrtact: Non - negative matrix factorization (NMF) is a method of parts - based feature extraction, it has been already applied to face recognition successfully.
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