svd(X) : 'x'里有无穷值或遗漏值
时间: 2023-05-29 18:05:43 浏览: 227
SVD (Singular Value Decomposition) 通常不适用于包含无穷值或遗漏值的数据。在这种情况下,需要进行数据清理或填充缺失值,以便使用SVD进行分解。一种常见的方法是使用数据插补技术来填充缺失值,如使用均值、中位数或回归模型来填充缺失值。如果数据中存在无穷值,则需要进行数据转换或处理,以确保数据包含有限值。
相关问题
cv::SVD::V_T
cv::SVD::V_T is a member function of the cv::SVD class in OpenCV library. It returns the transpose of the matrix of right singular vectors of the input matrix. The right singular vectors are the eigenvectors of the transpose of the input matrix multiplied by the input matrix. The transpose of the right singular vectors matrix is also known as the matrix of left singular vectors.
In other words, if we have a matrix A, and we perform the Singular Value Decomposition (SVD) on it: A=U*S*V_T, then cv::SVD::V_T returns the matrix of right singular vectors V_T.
Syntax:
cv::Mat cv::SVD::V_T
Returns:
A cv::Mat object representing the transpose of the matrix of right singular vectors of the input matrix.
Example:
```
cv::Mat A = (cv::Mat_<double>(3,3) << 1, 2, 3, 4, 5, 6, 7, 8, 9);
cv::SVD svd(A);
cv::Mat V_T = svd.vt;
```
This code snippet calculates the SVD of a 3x3 matrix A and returns the transpose of the right singular vectors matrix in V_T.
cv::SVD::compute(A, W, U, Vt, cv::SVD::FULL_UV | cv::SVD::vt)在opencv4.55怎么改写
在OpenCV 4.5.5中,可以使用以下方式进行SVD计算:
cv::Mat A = ...; // 输入矩阵
cv::Mat U, W, Vt;
cv::SVD::compute(A, W, U, Vt, cv::SVD::FULL_UV);
其中,cv::SVD::FULL_UV表示计算完整的SVD分解,不需要使用cv::SVD::vt标志。