传统的特征提取方法主要有：主分量分析、奇异值分解、投影追踪、自组织映射等。
Classical feature extraction methods include: Principle Component Analysis, Singular Value Decomposition, Projection Pursuit, Self-Organizing Map, and so on.
最小奇异值作为静态电压稳定指标已广泛应用于电力系统的稳定分析之中。
As an index of static voltage stability the minimum singular value is widely used in power system stability analysis.
提出了奇异值分解(SVD)和主分量分析(PCA)相结合的人脸识别算法。
A face recognition method based on the fusion of principal component analysis (PCA) and singular value decomposition (SVD) is presented.
介绍了基于动态系统可观测性矩阵奇异值分解的状态变量可观测度的分析方法。
The method of analyzing the observable degree of the state variable has been introduced by means of the singular value decomposition (SVD) of the observable matrix of a dynamic system.
推导了小波分析与奇异信号检测的之间的关系，并对某压力传感器的信号进行了奇异值的检测。
The relationship between wavelet analysis and singular signal detecting is deduced, and the example of a pressure sensor singular signal detecting is presented.
本文还就经验正交展开和奇异值分解方法在卫星高度计海面风、浪资料分析中的应用作了初步探时。
In this paper, also the primary applications of EOF and SVD in analyzing satellite altimeter data of sea surface wind and wave is studied.
一种新的颤振分析方法——鲁棒颤振裕度法被介绍，它利用结构奇异值理论将系统模型和试飞数据有机结合起来进行颤振边界预测。
Pass to incorporate organically the flight test data and the system model, make use of the structured singular value theories proceed the flutter boundary estimate.
使用谐波分析及奇异值分解(SVD)方法分析比较了北太平洋、热带太平洋区域的海气资料及海气相互作用。
Diagnostic analysis and contrast are performed in air-sea data and their interaction between the North Pacific and the Tropical Pacific by means of harmonic analysis and SVD.
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。
Singular Value Decomposition (SVD) is a dimension reduction method, and Symbolic data Analysis (SDA) is a new analytical approach to processing mass data.
其中人脸特征提取采用了奇异值分解和主分量分析法，身份验证则采用了以类内平均距离为判据的算法。
Here, we use the singular value decomposition and principal component analysis for facial feature extraction, using the average distance category as discrimination on the basis of authentication.
研究了一种适于工程实践的多变量稳定裕度法——基于回差矩阵奇异值的稳定裕度分析法。
The margin is evaluated by the minimum singular value of return deference matrix of system.
通过对信号与噪声奇异性的分析，得出信号与噪声的小波变换模极大值在各个尺度上的表现截然相反的结论。
The singularities of signals and noises are investigated. A conclusion is drawn that the wavelet modulus maxima of signals have contrary behaviors from that of noises.
理论分析表明，对广义分数低阶空时矩阵进行奇异值分解可获得噪声子空间估计。
Theoretical analysis shows that the matrix FSTM can be used to obtain the estimation of noise subspace.
在论述了小波分析、信号奇异性与小波变换模极大值性质关系的基础上，本文给出了一种基于小波变换模极大值的信号去噪算法。
Based on the wavelet transform and the relation between signal singularity and wavelet transform modular maximum, a noise removal technique is discussed.
基于奇异值分解滤波可以有效地分析水平(垂直)方向的图像特性。
Image filtering based on SVD favors the denoising in the line (horizontal) and column (vertical) direction.
基于奇异值分解滤波可以有效地分析水平(垂直)方向的图像特性。
Image filtering based on SVD favors the denoising in the line (horizontal) and column (vertical) direction.
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