The blueberry NMF can nourish the baby's tender skin.

蓝莓保湿因子滋养宝宝稚嫩的皮肤。

Analyzing the advantages and disadvantages of the NMF model.

分析了NMF模型的优点和不足之处。

Sodium PCA is thought to be a kind of natural moisturizing factor (NMF).

吡咯烷酮酸钠被认为是一种天然形成的滋润因素(NMF)。

Ingredients: Olive Extract, Mulberry Extract, Arbutin, Shea Butter, NMF, etc.

成份：橄榄精华萃取液，桑树提取液，熊果苷、乳木果油等。

Ingredients: mild cleaning factor, aqua moisturizing factor, NMF, silk peptide etc.

成份：温和清洁因子、水凝保湿因子、NMF、丝肽等。

To solve this problem to some degree, this paper purposes an improved NMF algorithm.

本文提出了基于音高修正基谱的方法，一定程度上改善了分离效果。

A new fusion of PCA and NMF combined with image fusing for face recognition method is proposed.

提出一种结合图像融合的PCA与NMF相融合的人脸识别的识别方法。

The results show that both cis - and trans - form of NMF can form a linear hydrogen bond with water.

计算结果表明，NMF的顺式和反式构型都可以与水分子形成线型的氢键结构。

Due to multiple solutions, the original algorithm of NMF is not suitable for resolving mixed chemical signals.

由于其多解的特征，文献介绍的NMF算法并不能直接用于化学混合信号解析。

After the ionization of the NMF-H 2o cluster, both the cis - and the trans-form will produce protonated products.

n甲替甲酰胺水团簇电离后，无论顺式和反式结构均有质子化产物生成。

The local feature based representation could be obtained by choosing suitable dimension of the feature subspace in NMF.

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。

Non-negative matrix factorization (NMF) has been proposed for multivariate data analysis, with non-negativity constraints.

非 负数据处理的一种多元统计分析方法。

Moreover, NMF algorithm is simple and easy to implement and it has features such as dimension-lowering and sparse convergence.

非负矩阵分解算法简单，易于实现，并且具有降维、收敛和稀疏等特性。

And in the feature extraction process, a new face recognition method based on CSVD and non Negative Matrix Factorization (NMF) is presented.

并在特征提取环节，提出CSVD算法与非负矩阵因子算法特征数据相融合的人脸识别算法。

Main compositions: Pomegranate essence, Inert type white VC, NMF aminophenol inductor, Extract of sweet tea, Ultra micro pearl powder, Unique factor.

主要成份：红石榴素、安定型净白vc、NMF氨基酸诱导体、甜茶萃取液、超微细珍珠粉、特制因子。

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.

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。

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。

The ORL database in experimental result with the alternative mean comparisons, indicates ICA/NMF unifies the method recognition rate must surpass the traditional method.

将ORL数据库上的实验结果同其他方法比较，表明ICA/NMF相结合的方法识别率要优于传统方法。

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.

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 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).

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。