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.
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