Feature dimension reduction can be divided into two categories: feature extraction and feature selection.
特征降维可以分为两类：特征抽取和特征提取。
Based on a detailed study of these processes, this thesis focuses on characteristics of feature dimension reduction and feature weighting.
本文在对这些过程进行详细了解和研究的基础之上，重点探讨了特征降维和特征加权过程。
Feature selection and input dimension reduction are of Paramount im-portance to transient stability assessment based on neural networks.
输入特征选择和输入空间降维是基于神经网络暂态稳定评估的首要问题。
Based on resolution reduction, neural network is then utilized for feature compression, which can effectively reduce the dimension of features.
在降低图像解析度的基础上使用神经网络来进行特征压缩，可以有效地降低特征维数。
This paper proposes two new methods: feature weighted likelihood and divergence based dimension reduction to improve detecting performance in noise.
本文提出了两种特征处理方法：特征的似然度加权和基于散度的维数缩减，来提高噪声下端点检测的性能。
Margin maximization feature weighting is an effective dimension reduction technique, and it is generally based on weighting techniques and similarity measure to construct their objective functions.
间距最大化特征选择技术是一种有效的维数约减技术，一般是基于加权技术和相似性度量构造目标函数。
In the domain of information retrieval, using feature clustering to extract the features is one of the most important means in the reduction of text dimension.
借助特征聚类进行特征抽取是信息检索领域进行文本特征降维的重要手段之一。
Feature space is high dimensional and sparse in text categorization, the process of dimension reduction is a very key problem for large-scale text categorization.
文本分类中特征向量空间是高维和稀疏的，降维处理是分类的关键步骤。
Dimension reduction techniques were discussed from the two aspects: feature selection and dimension transformation.
从属性选择和维变换两个方面对维规约技术进行了概括。
Dimension reduction techniques were discussed from the two aspects: feature selection and dimension transformation.
从属性选择和维变换两个方面对维规约技术进行了概括。
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