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.
本文借助于非负矩阵分解算法，提出了一种基于非负因子分析的模糊文本聚类方法。
Inspired by the nonnegative matrix factorization algorithm, we put forward an fuzzy text clustering method based on nonnegative factor analysis.
目的：研究非负矩阵因子分解算法(NMF)用于手性药物HPLC - DAD二维数据解析的可行性及其影响因素。
Aim: to study the feasibility and influential factors for the resolution of HPLC-DAD data of chiral drugs by non-negative matrix factorization (NMF) algorithm.
提出一种带有正则约束的非负矩阵分解算法(RCNMF)。
An algorithm with regularization constrains for nonnegative matrix factorization (RCNMF) is proposed.
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.
实验结果表明提出的两种特征提取方法在识别率方面整体上好于原非负矩阵分解特征提取（NMF）方法。
The experimental results on Olivetti Research Laboratory (ORL) face database and YALE face database show that the new methods are better than original NMF in terms of recognition rate.
本文主要是论述稀疏非负矩阵分解算法在矿产资源定量预测中的应用研究。
In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources.
其次，在第二章中，我们讨论了将一个非负矩阵分解成有限多个完全不可分非负阵的乘积的问题。
Secondly, we discuss the problem of decomposing a nonnegative matrix into a product of fully indecomposable nonnegative matrices in chapter 2.
非负矩阵分解（NMF）要求分解得到的左矩阵为列满秩，这限制了它在欠定盲分离（UBSS）中的应用。
The decomposed left matrix of Non-negative Matrix Factorization (NMF) is required to be full column rank, which limits of its application to Underdetermined Blind Source Separation (UBSS).
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.
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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