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。

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。

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。

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；

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。