
[数] 奇异值分解
The discrete model of the thermal dynamics process of precision machine was used to establish a new method called singular value decomposition algorithm for identifying these characteristics.
在精密机械热动态过程的离散化模型基础上,提出一种识别精密机械热动态特性参数的新方法——奇异值分解算法。
Fractal singular value neighbor distance is brought forward based on fractal neighbor distance. Fractal coding and local singular value decomposition are used to improve the recognition rate.
在分形近邻距离的基础上,提出了分形奇异值近邻距离,并把分形编码和局部奇异值分解结合起来,提高了识别率。
Low-rank estimation used the frequency correlation of the channel and singular value decomposition method.
低秩估计方法利用信道的频域(/时域)关性以及奇异值分解技术。
A robust stability boundary of uncertain singular systems is proposed by utilizing singular value decomposition and the character of mode matrix.
并利用奇异值分解方法和模矩阵的性质,给出了使不确定广义系统鲁棒稳定的一个鲁棒界。
And a method using iterative singular value decomposition (SVD) was presented for reducing the noise from a nonlinear time series to yield an improvement in the correlation dimension estimation.
采用迭代奇异值分解算法对含噪声的信号进行降噪,降低了噪声对相关维数计算结果的影响,从而提高了计算结果的可靠性。
An approach of suppressing the transient interference based on Singular Value Decomposition (SVD) is presented in this paper.
该文提出了基于矩阵奇异值分解的高频雷达瞬态干扰抑制方法。
In addition, a method based on singular value decomposition(SVD) was proceed to deal with the obtained result for dropping influence of noise.
为降低噪声的影响,采用一个基于奇异值分解(SVD)的方法对识别的结构进行处理。
In order to solve these problems, a rank-truncated multi-station TDOA localization algorithm based on singular value decomposition was presented.
为了解决这些问题,提出了一种基于奇异值分解的秩截短多站时差定位算法。
The Singular Value Decomposition (SVD) method for the equilibrium matrix is developed and a physical explanation is given.
引入了平衡矩阵的奇异值分解(SVD)方法并解释了其力学含义。
The problem of image matching and target tracking based on singular value decomposition (SVD) was discussed.
研究了基于奇异值分解的图像匹配和目标跟踪问题。
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。
And by the reduction of pilot numbers, frequency resource of the system is increased. In addition, with singular value decomposition, the algorithm computational complexity can be simplified.
通过简化导频数提高了系统频带利用率,利用奇异值分解进一步简化了算法复杂度。
Feature points' 3D coordinates are computed through singular value decomposition of projector matrix, then compute projector matrix by triangulation.
对应特征点的三维重建是根据三角测量的方法计算其投影矩阵,然后用奇异值分解求出特征点的三维齐次坐标。
To visualize the high dimension ellipsoidal RA approximately, a novel projection approach based on singular value decomposition for high dimension ellipsoidal RA is proposed in multi-machine case.
在多机系统中,应用奇异值分解方法,提出了高维椭球吸引域的三维投影方法,解决了高维椭球吸引域的直观显示问题。
Then, by the generalized singular value decomposition, a general symmetric solution of the minimum residual problem is obtained.
主要给出了矩阵的最小剩余问题及其最优近似问题的对称解。
A subspace-based tracking algorithm is proposed for high-resolution DOA estimation, using the generalized singular value decomposition (GSVD) of the sample data matrices.
本文论述了一种基于子空间方法的高分辨DOA估计跟踪问题的解法。
Statistical Process Control (SPC), Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) are introduced to det ermine APC opportunity and then APC structure.
这一研究对于开发和应用好先进控制技术具有重要的意义。
A novel method based on Bilateral Two-Dimensional Linear Discriminant Analysis(B-2DLDA) and symmetry average of local Singular Value Decomposition(SVD) for face recognition is presented.
针对人脸识别中光照、表情、姿态的影响,提出一种融合双向二维线性鉴别分析和局部对称平均的人脸识别新方法。
An algorithm for vehicle license plate tilt correction base on singular value decomposition;
该文提出基于字符投影点坐标方差最小的车牌垂直倾斜校正方法。
In this paper, an approach of soft-faults diagnosis based on characteristics extraction with singular-value decomposition (SVD) is presented.
利用奇异角特征方法能减少测试点数、压缩特征矢量维数和提高故障隔离率。
奇异值分解(Singular Value Decomposition, SVD)是线性代数中一种重要的矩阵分解方法,广泛应用于信号处理、数据降维和机器学习等领域。给定任意( m times n )的实数或复数矩阵( A ),其分解形式为:
$$ A = U Sigma V^T $$
其中:
参考来源:
奇异值分解(Singular Value Decomposition,SVD)是一种重要的矩阵分解方法,广泛应用于线性代数、数据科学和机器学习领域。以下是详细解释:
SVD将任意实数或复数矩阵 ( A )(( m times n ) 维)分解为三个特定矩阵的乘积: $$ A = U Sigma V^T $$
SVD揭示了矩阵的几何结构:
假设矩阵 ( A ) 为: $$ A = begin{bmatrix} 1 & 23 & 4 end{bmatrix} $$ 其SVD分解结果为:
SVD通过分解矩阵到其“核心成分”,提供了一种分析数据结构和降维的通用工具。其数学优雅性和实用性使其成为现代科学计算的基石之一。
【别人正在浏览】