NMF的翻译

The local concept semantics vector from NMF has stronger clssification capacity than that of global concept semantics, because the fromer can correspond directly with the sample characteristic, which manifests each classified text respective characteristic.

由于NMF生成的局部概念语义向量能和样本的特征直接对应，体现了各个分类中文本各自的特点，因此比体现所有文本共同特征的全局概念语义向量具有更强的区分能力。

First of all, we transform the Euclidean distance model into a sequence of least squares problems, from the non-negativity constrained least squares' KKT conditions we have a linear complementarity problem ( LCP), and then a new algorithm for NMF based on LCP is proposed.

首先我们把欧氏距离模型转化为序列非负最小二乘问题，然后根据非负最小二乘问题的KKT条件转化为线性互补问题，基于线性互补问题我们提出了一种NMF算法。

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

非负矩阵因子分解（non-negative Matrix Factorization，NMF）是对非负数据处理的一种多元统计分析方法。