An algorithm with regularization constrains for nonnegative matrix factorization (RCNMF) is proposed.
提出一种带有正则约束的非负矩阵分解算法(RCNMF)。
Non-negative matrix factorization has non-negative and local characteristics, and it is a new feature extraction method.
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。
In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources.
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。
Inspired by the nonnegative matrix factorization algorithm, we put forward an fuzzy text clustering method based on nonnegative factor analysis.
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。
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 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。
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。
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