查询词典 vector subspace

An iterative factorization method based on linear subspace for projective reconstruction is presented in the paper. It relies on the facts that the rows in the matrix including all the image points span the same linear subspace as the rows in the matrix including space points and the fact that any basis of the subspace can be regarded as projective reconstruction. The projective reconstruction and the depth factors are obtained based on linear iteration.

摘要该文提出了一种基于子空间线性迭代的射影重建方法，该方法利用所有的图像序列构成的行向量生成的线性子空间之和与射影重建结构点构成的行向量生成的子空间是同一线性子空间及在该子空间中任何一个基底都可以作为射影重建的特性，线性迭代地求取射影重建及图像深度因子。

The former uses Eigen-associative Learning algorithm to learn various facial expressions according to a face image with neutral expression. The latter is a two-level fusion approach which combines local linear and global nonlinear subspace learning. Amongst, the local linear subspace learning adopts eigen-representation technique for video sample compression in temporal domain; the global nonlinear subspace learning synthesizes optimized facial expressions in spatial domain.

前者采用特征关联学习算法根据一幅中性表情人脸图像合成具有其它表情的人脸图像；后者是一种结合局部线性和全局非线性子空间分析的两层融合方法，其中局部线性子空间学习采用特征表示技术在时间域对视频样本进行压缩、全局非线性子空间学习在空间域内产生优化的人脸表情。

In this paper, some decision theorems are suggested about the matrix orthogonal problem on the subspace of the finite dimensional Euclidan subspace. In addition, some concise results are obtained by matrix representation of the orthogonal complement and injection of vectors on the subspace.

利用向量组的Gram矩阵和向量的坐标矩阵对有限维欧氏空间中子空间的正交性给出了矩阵判定条件，对子空间的正交补及向量在子空间上的内射影给出了矩阵表示，获得了一些简捷有用的结果。

Looking upon the patches of face images as some specific class of signals, their eigen-subspace can be found by Principal Components Analysis, and the super-resolved facial patches are regularized in the orthogonal complement of the eigen-subspace. This eigen-subspace based regularization is combined with the classic reconstruction constraint under a Bayesian framework to produce the high-resolution outcome.

将人脸图像块看作一些特定信号类，利用主成分分析计算训练人脸图像块的特征子空间；并将传统的"重建约束"与人脸图像块的"正交补特征子空间约束"统一在贝叶斯框架下，提出一种人脸图像SR的规整化方法。

In this design, the dimension M of the memory vector field may be not necessarily equal to the dimension N of the network vector field. This may be the usually case in applications. Designing a network with either static or oscillating behavior, after analysing the dynamical. behavior of its four-dimension subspace, we get 2-torus in the subspace. If we vary the ratio of the imagine parts of the complex conjugate eigenvalues, the periodic motion of all kinds of period ratio on 2-torus are obtained.

设计一个既具有静态平衡点又具有动态振荡的网络，其静态平衡点由多余维叉形分叉得到，动态振荡形式由多余维Hopf分叉得到，通过对其中一个具有振荡行为的四维子空间的进一步分析，可以得到网络在四维子空间中的二环面运动，适当调整约当形中共轭复特征根虚部的比值，可以得到各种周期比的二环面上的周期运动。

The method relies on the facts that the row vectors composed of all the image points span, the same linear subspace as the row vectors composed of 3D space points span and that a basis of the subspace can consist of two row vectors composed of all the image points and one row vector in 3D space that is orthogonal to the former. The row vector can be determined, and the 3D reconstruction is accomplished. The novelty lies in the fact that the method can treat all the image points uniformly.

利用所有图像序列构成的行向量生成的子空间之和与三维空间点构成的行向量生成的子空间是同一线性子空间、同时由所有图像点构成的2个行向量外加一个行向量就可以组成该子空间的一个基底的特性，线性地求取子空间中的行向量，最后完成三维重建。

The covariance matrix of receive data is eigendecomposed. and the signal subspace is obtained. Then the channel vector is projected onto the subspace, and more accurate channel vector is obtained. According to the beamforming algorithm, the detection filter coefficients are calculated.

将接收数据的协方差矩阵进行特征值分解，得到信号子空间，将存在估计误差的信道矢量向该子空间投影，就可以得到较为准确的信道矢量，然后利用波束形成计算出检测滤波器的系数。

Instead of statistical description, a feature subspace with the base of some significant eigen vector extracted from covariance matrix is constructed to describe speech feature distribution of speaker. Distance metrics for measuring distance between input feature vector and subspace are also proposed for pattern matching.

说话人语音训练样本提取特征后在语音特征观察空间形成具有一定散度的分布，根据主元分析原理和分布散度提取主要散度本征向量作为基底构成说话人语音特征子空间，并通过测试语音特征矢量与子空间的距离测度进行模式匹配。

The coordinate transformation and numerical integration was executed on the discretized tetrahedral elements based on which the 3-D MT vector-finite element method was implemented. A whole computation framework for 3-D vector-finite element method with unstructured mesh was given. Based on this some typical models were tested which has demonstrated that our algorithm could distinctively avoid problems caused by the fake solution and both the accuracy and efficiency were enhanced which made our algorithm has a bright future for further application.3. According to theory of Sobolev vector space and the discretization of Helmholtz space, the error estimate which was suitable for 3-D MT vector-finite element modeling was deduced by which the procedure of adaptive technique was guaranteed.4. Based on the fully unstructured tetrahedralization and optical strategy, the 3-D magnetotelluric h-adaptive vector-finite element method was presented through combining the error estimate. With this work, the accuracy and creditableness for 3-D MT complicatedly modeling was guaranteed.5. The 3-D magnetotelluric h-adaptive vector-finite element algorithm with unstructured mesh was implemented.

针对非结构化的四面体单元，采用坐标变换和数值积分方法，实现了MT三维矢量有限单元分析，建立起基于非结构化网格的三维MT矢量有限元计算流程，并对典型模型和国际标准电磁模型进行了数值模拟，结果对比和分析表明，基于非结构化网格的三维MT矢量有限元不仅消除了节点型有限元的伪解，而且具有很高的计算精度和速度，有广阔的应用前景。3、根据Sobolev函数的向量空间和Hemlholtz空间的分解，推导出基于残差的三维大地电磁矢量有限元后验误差估计公式，为三维大地电磁自适应矢量有限元数值模拟的实现奠定了基础。4、在完全非结构化四面体单元剖分及优化基础上，结合三维大地电磁矢量有限元后验误差估计公式，提出了基于非结构化网格的三维大地电磁h-型自适应矢量有限元计算策略，保证了对复杂大地电磁模型数值计算的精度和可靠性。5、实现了基于非结构化网格的三维大地电磁h-型自适应矢量有限元计算流程，对典型模型和国际标准电磁模型进行了数值模拟。

In this step, the workpiece surface data such as the chamfering point P; the normal vector N; the feed vector F and the tool axis vector D are converted to the tool attitude data such as the tool axis vector D; the geometrical tool vector Tg and the functional tool vector Tf .

在这步，制件表面数据例如斜切的点P；正常传染媒介N；饲料传染媒介F和工具轴传染媒介D被转换成工具态度数据例如工具轴传染媒介D；几何工具传染媒介Tg和功能工具导航Tf。

Now she was hungry and angry.She began to smoulder.

现在她又饿又气，她开始流露难以抑制的怒火。

You have placed our iniquities before You, Our secret sins in the light of Your presence.

诗90：8 你将我们的罪孽摆在你面前、将我们的隐恶摆在你面光之中。