In this paper, by developing a method for updating eigen decomposition, we proposed an incremental Principal Component Analysis.
本文提出并推导了特征分解的校正算法，并以此为基础，实现了增量学习的主成分分析方法。
MUSIC (MUltiple SIgnal Characterization) is a special spectral estimation method based on the eigen decomposition of the sample covariance matrix.
多重信号分类(MUSIC)算法是通过对数据协方差矩阵进行本征分解获得信号空间谱估计的方法。
Through estimating the signal and noise subspaces with the eigen-decomposition of the correlation matrix, the MUSIC algorithm is used to estimate the DOAs of LFM sources.
通过对相关矩阵进行特征值分解，估计信号子空间和噪声子空间，并利用MU S IC算法估计宽带LF M信号的波达方向。
When the uncorrelated interferences come from same direction, the eigen-space decomposition adaptive algorithms just detect one interference, but the algorithms will suppress the interferences.
干扰不相关时，如果干扰来向相同，只能检测到一个干扰，但此时基于特征空间分解的自适应算法可实现干扰抑制。
In this method, eigen-decomposition does not have to be used, and the direction spectrum is the power spectrum in common sense (it can be used to estimate signal power).
本方法不需要特征分解，并且得到的是通常意义上的功率谱(可用于估计信号的能量)。
In this paper, a novel unconstrained cost function for the generalized eigen-decomposition is presented and the properties of the cost function are analyzed.
本文提出了一种新的无约束损失函数用于广义特征分解，并且分析了损失函数的特性。
The orthogonal projection (OP) adaptive beamforming algorithm has good performance but it is computationally expensive because of performing eigen-decomposition of the complex covariance matrix.
正交投影（OP）自适应波束形成算法性能优良，但需要进行复协方差矩阵特征分解，运算量大。
The traditional eight-point algorithm applied to two-view 3D reconstruction utilizes the standard Eigen Value Decomposition (EVD) algorithm.
传统的应用于双视图三维复原的八点算法使用标准特征值分析（EVD）算法。
The traditional eight-point algorithm applied to two-view 3D reconstruction utilizes the standard Eigen Value Decomposition (EVD) algorithm.
传统的应用于双视图三维复原的八点算法使用标准特征值分析（EVD）算法。
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