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。
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。
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。
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。
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。
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。
Thirdly, standardization and dimension reduction are performed to classify signals in each signal subspace. In the end, classification …
最后，用模糊积分将子空间分类结果融合，得出最终类。试验表明本算法速度较快、精确度高。
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。
A new method of text dimension reduction is brought forward, based on pattern aggregation and adaptive general particle swarm optimization (AGPSO).
将模式聚合和自适应广义粒子群算法相结合，提出了一种文本属性约简新方法。
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。
Based on dimension reduction, it puts forward a new indexing structure to improve the performance of content-based retrieval of large image databases.
在降维的基础上，建立了一个新的索引机制，并以此加速大规模图像库的基于内容检索的进程。
The thinking combined with various specific algorithm have many applications in other fields, including data Dimension reduction and regression forecast.
该思想与各类具体算法相结合在其他领域已有很多应用，其中包括了数据降维和回归预测。
Singular Value Decomposition (SVD) is a dimension reduction method, and Symbolic data Analysis (SDA) is a new analytical approach to processing mass data.
奇异值分解(SVD)是一种对数据进行降维处理的方法，符号数据分析(SDA)是一种处理海量数据的全新数据分析思路。
Moreover, utilizing the local geometry during ONPE dimension reduction, a new classification method (ONPC) based on a label propagation method (LNP) is proposed.
同时，在ONPE算法的基础上，利用局部几何信息，提出了一种在低维空间中使用标签传递(lnp)的分类算法——ONPC。
The constructed dimension reduction observer can estimate the vehicle state parameters depicting its steering performance in good agreement with the actual ones.
所构造的降维观测器也能使反映车辆操纵性能的车辆状态量估计值与实际值一致。
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。
This paper put its emphasis on dimension reduction, the main research contents are as following: the characteristic of hyperspectral remote sensing image is researched.
本文重点研究了高光谱遥感图像的降维方法，研究的主要内容如下：研究了高光谱遥感图像的特性。
Fuzzy rough set theory is an effective tool for reduction of data dimension, but there are few dimension reduction algorithms that are based on fuzzy rough set theory so far.
模糊粗糙集理论是解决数据集维数问题的有效工具，但基于模糊粗糙集的降维算法还不多。
The main contributions include: 1 a novel dimension reduction method, Supervised Latent Semantic Indexing SLSI, was proposed to represent documents for text classification tasks.
第一，提出一种有监督的潜在语义索引(SLSI)模型降维方法，用于文本分类任务中的特征表示。
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。
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。
The drastic reduction in the geometric dimension leads to great simplification in mathematical analysis.
几何维数的急剧减少导致数字分析的极大简化。
Reduction is used to decrease the dimension of structured data and the various compact degrees of data sets are obtained.
通过约简以减少结构化数据的维数，获得数据集合的不同简洁程度表示已成为数据挖掘的重要任务之一。
Reduction is used to decrease the dimension of structured data and the various compact degrees of data sets are obtained.
通过约简以减少结构化数据的维数，获得数据集合的不同简洁程度表示已成为数据挖掘的重要任务之一。
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