Multivariable robust adaptive backstepping control using matrix factorization 基于矩陣分解的多變量魯棒自適應(yīng)反推控制
Non - negative matrix factorization and its applications to gene expression data analysis 非負(fù)矩陣分解及其在基因表達(dá)數(shù)據(jù)分析中的應(yīng)用
Secondly , we utilize the nmf ( non - negative matrix factorization ) algorithm to extract human face local feature subspace 然后,對(duì)獲得的類人臉膚色區(qū)域利用nmf ( non - negativematrixfactorization )非負(fù)矩陣分解的方法提取人臉局部特征子空間。
The holistic features are extracted by principal component analysis ( pca ) , and the local features are extracted by non - negative matrix factorization with sparseness constraints ( nmfs ) 首先通過(guò)主元分析算法( pca )提取全局特征,利用帶稀疏限制的非負(fù)矩陣分解算法( nmfs )提取局部特征。
In this thesis , we mainly use snmf ( sparse nonnegative matrix factorization ) as the method of rank reduction , which extend the nmf to include the option to control sparseness explicitly 本文主要采用snmf (非負(fù)稀疏矩陣分解)算法作為降維和提取特征向量的工具,該算法是在nmf算法的基礎(chǔ)上加上顯式地稀疏因子控制而形成的一種非負(fù)矩陣分解方法。