linear subspace

Grouped linear subspace model was presented to make up disadvantages of linear subspace model while dealing with face images with shadows .

针对线性子空间模型在处理具有阴影的人脸图像时出现的不足之处，提出了GLS模型，并将其用于不同光照下的人脸识别。

Based on linear subspace , feature extraction becomes a popular way .

基于线性子空间上的特征提取技术是目前流行的方法。

Robust face recognition under various illuminations based on grouped linear subspace model

基于GLS模型的光照鲁棒性人脸的识别

Is a closed linear subspace of ( E , d ) .

本文证得若EC00那么SE是（E，d）的一个闭线性子空间。

Completely uncovered basis in linear subspace set

线性子空间的并集完全不覆盖的基

Experimental results show that it can give higher accurate recognition rate than linear subspace analysis methods and KPCA . 5 .

实验结果表明它能比线性子空间分析和核主元分析取得更好的识别性能。

When facial features are highly nonlinear distributed , linear subspace methods are difficult to extract effectively facial features .

当人脸特征呈现高度非线性分布时，线性子空间方法很难提取出有效的人脸特征。

The Polar Mapping and its Basic Properties of Every Linear Subspace of N-dimension Projective Space Pn

n维射影空间中线性子空间之间的配极对应及其基本性质

In this paper , the representation of metric projection on maximum linear subspace of Banach space is given .

本文利用对偶映射给出了Banach空间极大线性子空间上的度量投影的表达式。

Because it is inadequate for linear subspace analysis methods to describe the complex relations of real face images , such as pose , illuminant , expression variations .

由于线性子空间方法不足以描述实际人脸图像中的表情、光照、姿态等复杂的非线性变化。

Although Unsupervised Discriminant Projection ( UDP ) combines the local characteristics and nonlocal characteristics of samples , it is a linear subspace learning method in nature .

无监督鉴别投影技术（UDP）将样本的整体特征和局部特征有效地结合起来，但它本质上是一种线性的特征抽取算法，无法抽取样本的非线性特征。

Without loss of generality , the linear subspace learning algorithms can be explained as the enhancement of the affinity and repulsion of several data pairs in the low-dimensional space .

不失一般性，线性子空间学习算法也可以理解为在低维空间中对一系列数据对吸引和排斥关系的加强。

We proved that every closed maximal linear subspace in a Banach space is strongly orthogonally complemented if and only if the space X is reflexive and strictly convex .

证明了闭的极大线性子空间是强正交可补的充分必要条件是，空间x是自反严格凸的。

LDI algorithm finds the optimal linear subspace through solving a generalized eigenvalue problem , which can enhance the compactness of within class manifold and at the same time reduce overlaps between different classes .

LDI算法通过求解广义特征值问题得到一个在增强类内流形结构紧致性的同时减少不同类流形间交叠的最优线性子空间。

When the number of facial feature dimensions is much larger than the number of face samples , the subspace learned from the common linear subspace is not smooth and sparse solutions can not be obtained .

当人脸特征维数远远大于样本数目时，一般线性子空间方法学习得到的嵌入子空间非光滑且不能获得稀疏解。

It is assumed that the signal of interest belongs to a known linear subspace but that its coordinates within this subspace are otherwise unknown , and the interferences are also in some subspaces .

考虑一个具有空间特征的信号位于已知的子空间，但在子空间中的具体位置未知，并将干扰建模为子空间干扰。

So as to represent the nonlinear object , the quad-rotor , the work space of the quad-rotor is linearly divided , and in each linear subspace there is an ARX model to represent its characteristics .

其中，ARX模型是一种线性的局部模型，描述四旋翼这类非线性对象时，必须将其工作空间划分成局部子空间才可以表征其特征。

The work space of the quad-rotor is linearly divided , and in each linear subspace there is an ARX model to represent its characteristics . Then a appropriate switching mechanism is applied to achieve the goal of global model .

将四旋翼的工作区间进行划分，在每个单独的子区间内采用一个ARX模型来表征其特性，最后，通过合理的切换机制来实现全局的逼近目的。

The classical techniques for dimensionality reduction , PCA and CMDS , are simple to implement , and guaranteed to discover the true structure of data lying on or near a linear subspace of the high-dimensional input space .

经典的维数约减算法，如PCA和CMDS，实现简单，可以确保发现处于高维向量空间的线性子空间上的数据集的真实几何结构。

Dimension and Application Of Linear Map Subspace of Skew Field

再谈体上线性映射的子空间的维数及其应用

Paper [ 1 ] gave the dimension identity formula of linear map subspace of left vector space and discussed the theory application of matrix rank of skew field .

[1]中给出了左向量空间的线性映射的某些子空间的维数恒等式，并讨论了它在体上矩阵秩的理论上的应用。

And the nonlinear character of the algorithm improves the performance when there are short of pilots in OFDM systems . ( 3 ) Novel blind channel estimation is proposed by using linear pre-coding and subspace tracking techniques .

提出了基于预编码和子空间跟踪的信道盲估计算法。

It is proved that only the transformations on linear operators ' complementary subspace can be used to simplify high order normal form based on conventional normal form ( CNF ) .

在传统规范形基础上，证明了只有线性算子值域补空间上的近恒同变换才能化简高阶规范形。

Secondly , it describes the blind channel estimation algorithm of linear preprocessing and the subspace blind channel estimation algorithm in the blind channel estimation algorithm and analyzes their advantages and disadvantages after the corresponding simulation .

其次在基于盲信道估计的算法中介绍了基于线性预处理的盲信道估计和基于子空间的盲信道估计算法并做了相应的仿真，分析了其中的优缺点。