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.
非负矩阵分解算法简单，易于实现，并且具有降维、收敛和稀疏等特性。
Moreover, NMF algorithm is simple and easy to implement and it has features such as dimension-lowering and sparse convergence.
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.
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.
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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