What is the translation of " COVARIANCE MATRIX " in Chinese?

• 协方差矩阵

Examples of using Covariance matrix in English and their translations into Chinese

Quadratic form Covariance matrix.
二次型协方差矩阵.

Covariance matrix adaptation evolution strategy(CMA-ES).
协方差矩阵适应性进化策略（CMA-ES）.

Is the sample covariance matrix of the data.
其中是数据的samplecovariancematrix。

To make progress, we need to construct the covariance matrix.
为了方便计算我们需要构建协方差矩阵。

To sum up, the covariance matrix defines the shape of the data.
总而言之，协方差矩阵界定了数据的形状。

Specifically, this should be done before computing the covariance matrix.
特别地，这一步需要在计算协方差矩阵前完成。

However, the covariance matrix does not contain any information related to the translation of the data.
但是，协方差矩阵不包含数据变换相关的任何信息。

Where$\mathbf{S}$ is the sample covariance matrix of the data.
其中，$\mathbf{S}$是数据的协方差矩阵。

We can also express these results in terms of the corresponding partitioned covariance matrix.
也可以用对应的分区协方差矩阵来表达这些结果。

This means that we can represent the covariance matrix as a function of its eigenvectors and eigenvalues:.
这意味着我们可以将协方差矩阵表示为特征向量和特征值的函数：.

Figure 3 illustrates how the overall shape of the data defines the covariance matrix:.
图3展示了数据的整体形状如何定义协方差矩阵：.

If the covariance matrix is the identity matrix, the Mahalanobis distance reduces to the Euclidean distance.
如果协方差矩阵是单位矩阵，则Mahalanobis距离退化为Euc….

One way to address this problem is to use restricted forms of the covariance matrix.
解决这个问题的一种方式是使用限制形式的协方差矩阵。

(You might be able to guess that the covariance matrix is symmetric, which means that it doesn't matter if you swap i and j).
(你可能已经猜到协方差矩阵是一个对称矩阵，这意味着可以任意交换i和j)。

However, unlike IDA, QDA assumes that each class has its own covariance matrix.
然而，与LDA不同，QDA假设每个类别都有自己的协方差矩阵。

In an earlier article, we showed that the covariance matrix can be written as a sequence of linear operations(scaling and rotations).
在另一篇文章中，我们表明协方差矩阵可以写为一系列线性操作（缩放和旋转）。

However, unlike IDA, QDA assumes that each class has its own covariance matrix.
但是，与LDA不同的是，QDA假设每个类别具备自己的协方差矩阵。

Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters.
不同于以往研究中提出的协方差矩阵估计方法，新提出的这两种方法同时考虑了项目参数以及结构参数。

Thus, this multivariate Gaussian model would have x and μ as vectors of length d, and Σ would be a d x d covariance matrix.
因此，这个多元高斯模型将x和μ作为长度d的向量，∑将是一个d×d协方差矩阵。

However, the‘eigen' solver needs to compute the covariance matrix, so it might not be suitable for situations with a high number of features.
然而，该解决方案需要计算协方差矩阵，因此它可能不适用于具有大量特征的情况。

SVD is essentially a way to calculate ordered components too, but you don't need to get the covariance matrix of points to get it.
SVD本质上也是一种计算有序分量的方法，但是你不需要得到点的协方差矩阵就可以得到它。

In this article we showed that the covariance matrix of observed data is directly related to a linear transformation of white, uncorrelated data.
在本文中，我们表明观察到数据的协方差矩阵与白色不相关数据的线性变换有直接的关系。

Note that the new mean only depends on the conditioned variable, while the covariance matrix is independent from this variable.
要注意的是，新的均值只依赖于作为条件的变量，而协方差矩阵则和这个变量无关。

The covariance matrix will not only describe the shape of our distribution, but ultimately determines the characteristics of the function that we want to predict.
协方差矩阵不仅仅描述了这个分布的形状，也最终决定了我们想要预测的函数所具有的特性。

SVD is essentially a way to calculate ordered components too, but you don't need to get the covariance matrix of points to get it.
此外，SVD本质上也是一种计算有序成分的方法，但你不需要通过获取数据点的协方差矩阵来实现它。

The covariance matrix of this‘white' data equals the identity matrix, such that the variances and standard deviations equal 1 and the covariance equals zero:.
这个“白色”数据的协方差矩阵等于单位矩阵，使得方差和标准差等于1，协方差等于零：.

More precisely, the Maximum Likelihood Estimator of a sample is an unbiased estimator of the corresponding population covariance matrix.
更准确地说，样本的最大似然估计是相应的总体协方差矩阵的无偏估计。

Recovery is easier from a correlation matrix than a covariance matrix: standardize your observations before running GraphLasso.
从相关矩阵中恢复比从协方差矩阵中容易：在运行GraphLasso前对观测样本进行标准化。

We now have a prediction matrix which gives us our next state, but we still don't know how to update the covariance matrix.
现在，我们有了一个预测矩阵来表示下一时刻的状态，但是，我们仍然不知道怎么更新协方差矩阵。

Many statistical problems require the estimation of a population's covariance matrix, which can be seen as an estimation of data set scatter plot shape.
许多统计问题都需要估计种群的协方差矩阵，这可以看作是对数据集散点图形状的估计。