对于大稀疏矩阵，在计算中保持矩阵的稀疏性是很重要的。
For large sparse matrix, it is very important to keep the sparse of matrix in computation.
该矩阵由于其构成的非常稀疏性大大简化了图像重建过程中的投影计算，从而提高重建速度。
Owing to its sparsity of structure, new matrix greatly simplifies the projection operation during images reconstruction, which greatly improving the speed of reconstruction.
对于所求解方程的系数矩阵的高度稀疏性，给出了紧缩存储算法，节省了存储空间和减少了计算量。
Because the coefficient matrix of the equation to be solved is very sparse, the algorithm with the compact storage scheme is given and the computation cost is also reduced.
针对语音信号的弱稀疏性，提出一种新的基于混合矩阵估计的欠定语音盲分离方法。
This paper proposes a new method based on mixing matrix estimation for underdetermined blind speech separation, aiming at speech signals under weak sparseness.
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。
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.
利用稀疏分量的直线聚类性，提出了欠定盲源分离中估计混合矩阵的一种方法。
A method of the mixing matrix estimation in underdetermined source separation is proposed, which is based on the linear clustering of sparse component.
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。
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.
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。
And point out the problems existed in the traditional collaborative filtering methods: ratings matrix sparsity, cold start, vulnerability, single interest model and scalability issues.
针对管网模型中雅克比矩阵高稀疏性的特点，采用三元组顺序表只对矩阵中的非零元素进行存储，提高了程序的存储和计算效率。
Triple table is used to only store non-zero matrix elements to improve the program's efficiency of storage and computation because simulation model's Jacobian matrix is serious sparse.
针对管网模型中雅克比矩阵高稀疏性的特点，采用三元组顺序表只对矩阵中的非零元素进行存储，提高了程序的存储和计算效率。
Triple table is used to only store non-zero matrix elements to improve the program's efficiency of storage and computation because simulation model's Jacobian matrix is serious sparse.
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