A tensor matrix orthogonal diagonalization is utilized for image separation.
提出对照函数为二次特征函数的四阶导数,采用特征函数的高阶导数的矩阵张量方法,通过正交对角化实现图像的瞬时盲分离。
The conditons and realization of sub-diagonalization and orthogonal sub-diagonalization of matrices;
矩阵的次对角化和正交次对角化的条件及实现
The article gives a result of the orthogonal sub-diagonalization of skew-symmetric trabsformation,and proves this discussion.
我们将给出反对称变换都可以正交次对角化,并证明这一结论。
We introduce a new kind of diagonalization for matrices-orthogonal sub-diagonalization, and find out a sufficient condition in which matrices can be orthogonally sub-diagonalized.
引进矩阵的一种新的“对角化”——正交次对角化的概念 ,并找出了矩阵可以正交次对角化的充分条件 。
From given eigenvalues and eigenvectors, the inverse eigenvalue problem of symmetric ortho-symmetric positive semi-definite matrices and its optimal approximate problem were considered.
对给定的特征值和对应的特征向量,提出了对称正交对称半正定矩阵逆特征值问题及最佳逼近问题。
