High dimensionality is one of the main problems in text categorization.
文本分类中特征空间的高维问题是文本分类的主要障碍之一。
High dimensionality is the main difficulty of similarity search over time-series data.
数据的高维度是造成时序数据相似性搜索困难的主要原因。
"High dimensionality and small size samples" is widely encountered in many real world machine learning applications.
“高维度小样本”问题是模式识别应用中的主要障碍之一。
Power system stability analysis is extremely complex due to its multi time scale nature, high dimensionality and non linearity.
电力系统是一个多时间尺度的大规模非线性系统，其稳定性分析非常复杂。
Power system stability analysis is extremely complex due to its multi-time scale nature, high dimensionality and non-linearity.
由于电力系统是一个多时间尺度的高阶非线性系统，其稳定性分析就变得异常复杂。
This paper introduces a fast algorithm to cluster large binary data sets where data points have high dimensionality and most of their coordinates are zero.
文介绍了一种聚类大型二元数据集合的快速算法，在该数据集合中数据点是高维的，并且大多数的坐标值为零。
Although numerical simulation method has wide applicability, its efficiency is low, especially for high dimensionality and small failure probability problems.
尽管数字模拟法适用范围广，但该方法的效率较低，尤其是针对高维和小失效概率问题。
Under the condition of high dimensionality, it converges faster than the common simplex method, while its calculating work and storage space required are reduced greatly.
它的收敛速度在高维情况下优于普通的单纯形法，同时计算量和存贮量均大大减少。
With the growth of data in volume and dimensionality, it has become a very challenging problem to build a high-efficient classifier for large databases.
随着数据集的数据量和维数的增加，建立高效的、适用于大型数据集的分类法已成为数据挖掘的一个挑战性问题。
But the dimensionality of the original face image is too high to use LDA method directly.
但人脸的原始特征空间维数很高，无法直接利用LDA方法提取分类特征。
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。
Part 2 is in the Projection Pursuit ideology high-dimensional data principal component analysis dimensionality reduction theoretical analysis and practical application;
第2部分进行的是在投影寻踪思想下对高维数据主成分分析降维的理论分析和实践应用；
The Locaally linear Embedding (LLE) algorithm is an effective technique for nonlinear dimensionality reduction of high-dimensional data.
局部线性嵌入(LLE)算法是有效的非线性降维方法，时间复杂度低并具有强的流形表达能力。
Genetic algorithms are good candidates for attacking the challenge since GAs are very useful for extracting patterns in high-dimensionality problems where heuristic knowledge is sparse or incomplete.
由于在缺乏启发性的知识的情况下，遗传算法对于高维的数据的特征提取非常有效，所以遗传算法是基因数据研究中非常重要的算法。
Most of the real-world data, such as images and videos, are always represented by tensor and high-dimensionality form, which make tensor data dimensionality reduction the hot issue in recent research.
实际应用中的许多数据，如图像，视频，通常具有张量性和高维性特征，张量数据的维数约简便成为近期的研究热点。
Most of the real-world data, such as images and videos, are always represented by tensor and high-dimensionality form, which make tensor data dimensionality reduction the hot issue in recent research.
实际应用中的许多数据，如图像，视频，通常具有张量性和高维性特征，张量数据的维数约简便成为近期的研究热点。
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