边界单元法降低了求解空间的维数，减少了离散线性方程组的阶数，输入数据比较少，而工作效率高。
This method reduces the number of space dimension, makes the equation system lower order, less data input and higher efficiency .
应用主元分析方法将高维数据转换到低维数据空间，这使得过程监测可以在低维的空间内进行。
High dimension was changed into low dimension by using principal component analysis method, process detecting could be carried out in the low dimension space.
商空间理论在处理高维、不完备、复杂的、模糊的、海量数据时，有其独特的优势。
Quotient space theory for treatment of high-dimensional, incomplete, complex, vague, massive data, there are unique advantages.
它将高维输入空间的数据映射到一个低维、规则的栅格上，从而可以利用可视化技术探测数据的固有特性。
It projects input space on prototypes of a low - dimensional regular grid that can be effectively utilized to visualize and explore properties 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。
It is hard to cluster high-dimensional data using traditional clustering algorithm because of the sparsity of data.
投影寻踪方法是根据特定的应用意义设计相应的投影指标，把高维数据集投影到低维数据空间后进行分析，揭示高维数据集内部的结构和特征。
By designing projection index it projects high dimensional data set to low dimensional space to reveal the internal structures and characters of high dimensional dataset.
树型空间索引可以高效地组织并检索高维数据，因此使用树型空间索引是改善聚类性能的有力途径。
The structures and performances of all kinds of tree-like spatial indexes are analyzed in this paper.
针对高维数据的相似性度量问题，提出了一种基于子空间的相似性度量方法。
Aiming at the similarity measurement of high dimensional data, the paper put forward a new method based on subspace.
在低维空间描述高维数据是数据分析、模式识别、机器学习、计算机视觉等领域的基础问题之一。
Representation the high-dimensional data in a low-dimensional subspace is one of the fundamental problems in data analysis, pattern recognition, machine learning, and computer vision.
通过对样本数据空间的主成分分析，能够保证在信息损失最少的情况下，对高维变量空间进行降维处理，减少样本数据间的相关性。
To analysis the sample data space by PCA can assume that it can lower the dimension of high variant space and eliminate the relativity of sample data.
摘要：高维数据之间的相似性度量问题是高维空间数据挖掘中所面临的问题之一。
Absrtact: the problem of similarity measurement between high dimensional data is one of the problems high-dimensional data mining faces.
计算主成分的目的是将高维数据投影到较低维空间。
The results of a PCA are usually discussed in terms of component scores and loadings (Shaw, 2003).
计算主成分的目的是将高维数据投影到较低维空间。
The results of a PCA are usually discussed in terms of component scores and loadings (Shaw, 2003).
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