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。
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。
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。
General solutions of above inverse problem in positive definite matrix and in orthogonal matrix are given here by using factorization method of matrix.
本文用矩阵分解法给出该反问题在正定矩阵类及正交矩阵类中的通解。
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。
If the transform matrix type is a half pixel matrix, then the method includes applying a factorization technique to decode the bit stream corresponding to the half pixel matrix.
若该变换矩阵的类型是半像素矩阵，则该方法应用因子分解技术解码与该半像素矩阵对应的比特流。
If the transform matrix type is a half pixel matrix, then the method includes applying a factorization technique to decode the bit stream corresponding to the half pixel matrix.
若该变换矩阵的类型是半像素矩阵，则该方法应用因子分解技术解码与该半像素矩阵对应的比特流。
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