The sparse vector method enhances the efficiency of matrix solution algorithms by exploiting the vector sparsity. It has been successfully applied to many problems arising in power systems.
稀疏向量法通过利用向量的稀疏性来提高求解矩阵方程的效率，它被成功地应用到电力系统分析的众多问题。
A dense matrix arising from MoM can be stored in sparsity after compressed.
一个由矩量法生成的稠密矩阵经过压缩后，可以稀疏存储。
Whole sparsity and local sparsity are proposed aiming at the sparsity of SAR images.
并针对SAR图像的稀疏性，提出了整体稀疏性与局部稀疏性的概念。
The theme for this thesis is to exploit sparsity in a number of wireless communication problems.
本论文中我们利用信号的稀疏性解决一系列无线通信中的问题。
The sparsity of SAR image data was discussed based on the backscattering properties of targets in the image.
利用合成孔径雷达（SAR）图像中目标的后向散射特性和目标散射中心的理论，分析了SAR图像数据稀疏性的成因。
It is hard to cluster high-dimensional data using traditional clustering algorithm because of the sparsity of data.
在高维空间中，由于数据的稀疏性，传统的聚类方法难以有效地聚类高维数据。
Unlike conventional deconvolution methods, the BPD USES an L1 norm constraint on model reflectivity to impose sparsity.
不同于传统的反褶积方法，BPD使用L1范数约束模型的反射率来增加了稀疏性。
Translation template can solve the problem of data sparsity, large storage space and low matching precision of examples.
利用翻译模板可以有效的解决翻译实例的数据稀疏问题、简化实例库的规模并提高实例匹配的精确率。
The improved methods can effectively alleviate the problem of sparsity and improve the quality of recommendation system.
本文提出的算法能够有效缓解数据稀疏性问题，提高推荐系统的推荐质量。
In research, sample data were mapped to sparse feature space to prevent the loss of SVM's sparsity when the kernels were fused.
为避免在进行核融合时，支持向量机稀疏性的缺失，提出将数据映射到稀疏特征空间进行研究。
Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
针对协同过滤中的数据稀疏问题，提出了一种基于粗集的协同过滤算法。
Using the sparsity of the scene, pixels above the threshold were mapped to a triple before entropy encoding for both grayscales and locations.
利用场景的稀疏性，将阈值以上的像素映射到三元组，对其灰度和位置信息分别熵编码；
Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.
现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。
This method keeps the patches pairs the same sparsity patterns, and makes efficiently use of the self-similar information. The adaptability is enhanced.
该文方法保证跨尺度自相似集具有相同的稀疏性模式，能更有效地利用图像的自相似性先验信息，提高算法的自适应性。
The magnitudes of items and users in the system results in the extreme sparsity of user rating data, which makes it difficult to find neighbors effectively.
随着系统中资源数目和用户数目的不断增加，在整个资源空间上用户评分数据极端稀疏，给有效的查找最近邻居带来了很大的困难。
Owing to the sparsity of high-dimensional data and the features of categorical data, it needs to develop special methods for high-dimensional categorical data.
现有的数据聚类方法仍存在着各种不足，聚类速度和结果的质量不能满足大型、高维数据库上的聚类需求。
For a general banded matrix, discuss the sparsity pattern of the Q and R matrices from the QR decomposition of symmetric and non-symmetric tridiagonal matrices.
对于一般的带状矩阵，详述对对称与非对称三对角化矩阵做QR分解后，Q矩阵与R矩阵的稀疏元素分布型态。
This algorithm is an improvement of local sparsity ratio and local sparsity coefficient computation for local sparsity coefficient - Based (LSC) Mining of Outliers paper.
该算法是对基于局部稀疏系数(LSC)孤立点挖掘论文中局部稀疏率和局部稀疏系数计算的一种改进。
The sparsity and the problem of the curse of dimensionality of high-dimensional data, make the most of traditional clustering algorithms lose their action in high-dimensional space.
高维数据的稀疏性和“维灾”问题使得多数传统聚类算法失去作用，因此研究高维数据集的聚类算法己成为当前的一个热点。
According to compressive sensing theory, encoding a video sequence into a few pseudo-random projections of each frame is reasonable due to the sparsity in the interested information.
根据压缩感知理论，用每帧的少量伪随机投影编码一个视频序列。
And point out the problems existed in the traditional collaborative filtering methods: ratings matrix sparsity, cold start, vulnerability, single interest model and scalability issues.
指出了传统协同过滤方法存在着评分矩阵稀疏、冷启动、易受攻击性、兴趣模型单一和可扩展性等问题。
According to signals sparsity by Curvelet transform, the mixed matrix can be estimated with C-means cluster analysis, and the estimated value is looked as initial value of BSS algorithm.
该方法利用Curvelet多尺度几何分析后信号的稀疏性特点，采用了C - means聚类方法寻求混合矩阵估计值，把该估计值作为算法初始值。
However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items.
但是随着用户数量和系统规模的不断扩大，协同过滤推荐技术将面临严重的数据稀疏性、超高维、冷启动和实时推荐等方面的挑战。
However, collaborative filtering has got challenges, such as data sparsity, high dimensions, cold start, and real-time recommendation issues with the fast growth in the amount of users and items.
但是随着用户数量和系统规模的不断扩大，协同过滤推荐技术将面临严重的数据稀疏性、超高维、冷启动和实时推荐等方面的挑战。
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