关键词: 线性判别分析, 邻域保持判别分析, 局部保持投影, 非负矩阵分解

Abstract: Nonnegative Matrix Factorization（NMF） is a new matrix decomposition technique.A modified NMF algorithm called Neighborhood Preserving Discriminant Nonnegative Matrix Factorization（NPDNMF） is proposed for enhancing the classification accuracy of the NMF algorithm.Neighborhood Preserving Discriminant Analysis（NPDA） is a method constructed by combining the ideas of both Linear Discriminant Analysis（LDA） and Local Preserving Projection（LPP），which can hold the strong power of LDA and preserve the intrinsic geometry of the data samples.The proposal incorporates the discriminant constraints of NPDA inside the NMF decomposition，which yields a part based decomposition with enhanced discriminant power.The experiment on ORL face database for face recognition shows that the proposal obtains a better performance.

Key words: Linear Discriminant Analysis（LDA）, Neighborhood Preserving Discriminant Analysis（NPDA）, Local Preserving Projection（LPP）, Nonnegative Matrix Factorization（NMF）