matrix factorization造句
例句與造句
- 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
非負矩陣分解及其在基因表達數(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,對獲得的類人臉膚色區(qū)域利用nmf ( non - negativematrixfactorization )非負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 )
首先通過主元分析算法( pca )提取全局特征,利用帶稀疏限制的非負矩陣分解算法( 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矩陣分解方法。 - It's difficult to find matrix factorization in a sentence. 用matrix factorization造句挺難的
- The traditional methods are to solve the linear algebra equations directly , based on matrix factorization such as lu decomposition . with this kind of methods , the " true " solution can be derived if there is no consideration of the round error
解線性代數(shù)方程組的傳統(tǒng)方法是利用lu分解等直接求解，雖然傳統(tǒng)方法具有理論上直接得到真解的優(yōu)點，但當(dāng)系數(shù)矩陣條件數(shù)很大時，存在嚴(yán)重的穩(wěn)定性問題。 - Principle component analysis ( pca ) , as a classical method for feature extraction , learns holistic representations of facial images , while non - negative matrix factorization ( nmf ) , a recently proposed approach , learns parts - based representations of faces . however , we argue that nmf can not only learn parts - based representations but also holistic ones with different sparseness constraints
在眾多的特征提取算法中,基于全局特征提取的主元成分分析( principlecomponentanalysis , pca )是討論最多的經(jīng)典算法,與此對應(yīng)的是基于局部特征提取的非負矩陣分解( non - negativematrixfactorization , nmf )算法。 - In this thesis , we propose an efficient nmfs + rbf aggregate framework for fr , in which non - negative matrix factorization with sparseness constraints ( nmfs ) is firstly applied to learn either the holistic representations or the parts - based ones by constraining the sparseness of the basis images , and then the rbf classifier is adopted for pattern classification
本文提出了一種基于非負矩陣稀疏分解( non - negativematrixfactorizationwithsparsenessconstraints , nmfs )和rbf神經(jīng)網(wǎng)絡(luò)的人臉識別方法。通過控制稀疏度, nmfs算法既可提取人臉全局也能提取局部特征,再運用rbf神經(jīng)網(wǎng)絡(luò)進行模式分類。 - Different from other rank reduction methods , such as pca ( principal component analysis ) and vq ( vector quantization ) , nmf ( nonnegative matrix factorization ) can get nonnegative , sparse basis vectors which make possible of the concept of a parts - based representation
與pca (主分量分析)和vq (矢量量化)等降維算法不同, nmf (非負矩陣分解)算法能夠分解出非負的,稀疏的特征矩陣和編碼矩陣,能夠提取原始數(shù)據(jù)向量的局部特征,使基于局部特征進行分類的聚類算法更容易實現(xiàn)。