使用伪词可以避免有指导的词义消歧方法中的数据稀疏问题，充分验证词义分类器的实验效果。
Using pseudowords we can overcome data sparseness problem in supervised WSD and fully verify the experimental effect of word sense classifier.
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。
Aiming at the problem of data sparsity for collaborative filtering, a novel rough set-based collaborative filtering algorithm is proposed.
现有平滑技术利用不同的折扣和补偿策略来处理数据稀疏问题，在计算复杂性与合理性方面各有其优缺点。
The present smoothing techniques deal with the data sparse problem using different discount and compensate strategy, and they have different merit or shortcoming on complexity and rationality.
现有平滑技术虽然已有效地对数据稀疏问题进行了处理，但对已出现事件频率分布的合理性并没有作出有效的分析。
The present smoothing techniques have solved the data sparse problem effectively but have not further analyzed the reasonableness for the frequency distribution of events occurring.
但是随着研究深入，出现稀疏数据成像问题，无法用传统方法重建清晰图像。
With the in-depth study, however, appearing sparse data imaging, which traditional methods can not used to reconstruct a clear image.
本文利用三元模型，通过引入相似词，采取“词形-相似词-词性”三步回退的策略，比较好地缓解了数据稀疏问题。
Based on trigram models, this paper proposes a three-step method of "word-similar word-part of speech" by incorporating the similar words and solves the problem of sparse data to a large extent.
信号的稀疏表示或最佳n -项逼近是数据压缩、噪声抑制等众多应用中的一个重要问题。
Signal sparse representation or the optimal N-term approximation is one of the important problems, which is applied to many areas such as the data compression, denoising.
然后将其与地震子波褶积，使其求解结果与实际地震数据的最小平方问题归结为求解一大型稀疏矩阵方程，并采用奇异位分解法求解。
The least square problem of the convolution result and real seismic data can be considered as the solution of a huge rarefactional matrix equation, which can be solved by singular value decomposition.
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。
Translation template can solve the problem of data sparsity, large storage space and low matching precision of examples.
这两种方法均不受外部资源所限，能在一定程度上解决数据稀疏问题。
These two methods are both not subject to external resource constraints and would solve the data sparse problem in some extent.
针对高属性维稀疏数据聚类问题，提出高属性维稀疏信息系统概念，给出一种新的基于稀疏特征差异度的动态抽象聚类方法。
The concepts of high attribute dimensional information system are firstly proposed, and a new dynamic clustering method on the basis of sparse feature difference degree is presented.
LPLE算法解决了传统LLE算法在源数据稀疏情况下的不能有效进行降维的问题，这也是其他传统的流形学习算法没有解决的。
LPLE is better than LLE in that it gives the global coordinates of the sparse data and this isn't be resolved by the other conventional algorithm.
电子商务系统规模的日益扩大，协同过滤推荐方法也面临诸多挑战：推荐质量、可扩展性、数据稀疏性、冷开始问题等等。
But, with expansion of E-commerce system's size, collaborative filtering approach suffer from many challenges, for instance, quality of recommendations, scalability, sparsity, cold-start problem.
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。
The improved methods can effectively alleviate the problem of sparsity and improve the quality of recommendation system.
传统的协同过滤主要存在着：精确性、数据稀疏与冷启动的问题。
The traditional Collaborative Filtering has shortcoming as follows: accuracy, data sparse and cold-start.
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。
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.
协同过滤是目前应用较为成功的信息推送技术，但也遇到了数据稀疏性、冷启动等种种问题。
Collaborative filtering is a successful technology in information push, but this method has encountered data sparse, cold start and other issues.
但是随着研究深入，出现稀疏数据成像问题，无法用传统方法重建清晰图像。
The key problem in N-gram method is the problem of sparse data which still can not be solved effectively now.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
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