Naturally keep wet factor nmf can supply water for the skin continuously , make your skin moisten and pleasant sense 天然保湿因子nmf ,持续给皮肤进行补水,肌肤瞬间感觉到水水嫩嫩,使你润肤后倍感舒爽。
Contains unique nmf elements and vitamin a that can penetrate into deep inside the skin , make skin experience the outstanding moisture and smooth feelings 蕴含独有的nmf成份及维他命a ,能渗入肌肤深处,使肌肤体验到前所未有的湿润光滑。
Secondly , we utilize the nmf ( non - negative matrix factorization ) algorithm to extract human face local feature subspace 然后,对获得的类人脸肤色区域利用nmf ( non - negativematrixfactorization )非负矩阵分解的方法提取人脸局部特征子空间。
The jidm work group of nmf is working for integrating cmip > snmp and corba . the integration of cmip > snmp and corba is the trend of network management 网络管理的趋势是集成cmip 、 snmp协议和corba技术,从而充分利用标准网络管理协议和分布对象计算的优势。
Our experimental evaluations show that our methods surpass the nmf not only in the easy and reliable derivation of document clustering results , but also in document clustering accuracies 实验结果显示,在聚类的容易度、准确度、时间复杂度上均取得较nmf算法更合理的效果。
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 (非负稀疏矩阵分解)算法作为降维和提取特征向量的工具,该算法是在nmf算法的基础上加上显式地稀疏因子控制而形成的一种非负矩阵分解方法。
Different from previous document clustering method based on nmf , our methods try to discover both the geometric and discriminating structures of the document space in an unsupervised manner , companied with high accuracy in acceptable computationally expensive 与基于nmf算法的文本聚类不同,我们的算法力求以无监督的方式,在时间复杂度允许的范围内,找到更适合于分类操作的数据向量间的几何局部特征向量及相应的各文档的编码向量。
It contain nmfs lock water keeping warms factor vitamin amino acids , trace element , in the hair surface form moist membrane , keeping wet bright gloss , and it can deepen each one hair . adopted unique water dissolved prescription , it s cleanlily , naturally and not greasy , bring you a effect of graceful and lenitive finalizing the design enduringly 蕴含nmf锁水保温因子维他命氨基酸微量元素,在发丝表面形成滋润膜,保持秀发湿亮光泽,能深入每根发丝,彩用独特的水溶配方,清爽自然不油腻,带给你丰盈润泽的持久定型效果。
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 在众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 )是讨论最多的经典算法,与此对应的是基于局部特征提取的非负矩阵分解( non - negativematrixfactorization , nmf )算法。