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。
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。
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。
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。
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。
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。
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。
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。
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。
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。
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