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1·The dimension reduction of hyperspectral data was classified by SVM.

对降维后的高光谱数据采用SVM进行分类。

2·In this paper, we propose a dimension reduction method based on the tangent bundle.

本文提出了一种基于切丛的维数约简方法。

3·In the end, for the extracted features, we used PCA for dimension reduction and SVM for recognition.

最后对于所提取的特征利用PCA降维后送入支持向量机中分类。

4·PCA and ICA are adopted to deal with dimension reduction, the effect of which is compared and analyzed;

对比分析了PCA、ICA进行故障数据降维处理的效果并结合其优点进行数据降维处理；

5·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。

6·The minimum number of causality judgement indicators of ADR was determined by dimension reduction method.

运用降维技术探讨adr因果关系判断的最小因子数。

7·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。

8·The dimension reduction method is discussed theoretically and the computer implementation of the method and its results are given.

首先理论上讨论降维法，其次给出计算机程序实现的方法及其结果。

9·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。

10·To reduce the complexity, dimension reduction executes before ICA algorithm, and then after iteration of the orthogonal data processing.

为了降低复杂度，在进行ICA运算时，先对接收数据进行降维预处理，然后对迭代后的数据进行正交化处理。