美 英 un.矩阵的主对角线 英汉 un. 1. 矩阵的主对角线
If A is a skew-symmetric matrix and n is odd positive integer, then A^... 01:15 Show that the elements on the main diagonal of a skew-symmetric mat... 07:12 In a skew-symmetric matrix, every principal diagonal element is 01:30 Show that the elements on the main diagonal of a sk...
A square matrix is an upper triangular matrix if all elements below the principal diagonal are zero. So a 2* 2 upper triangular matrix has the formA=(bmatrix) a&b 0&d(bmatrix) where a, b, and d are any real numbers. Discuss the validity of each of the following statements. If th...
In this paper, for each and every strongly connected component in a digraph, we add weights to diagonal elements of its member nodes in the adjacency matrix such that the modified matrix will have the new unique largest eigenvalue and corresponding principal eigenvectors. Consequently, we use the...
I f not, give examples.I f A an d B are 2 × 2 diagonal matrices, then A B = BA. 2【题目】 A square matrix is a diagonal matrix if all elements not onthe principal diagonal are zero. So a 2 × 2 diagonal matrixhas the formA=[&a&0&0%]where a an d d are real numbers....
After estimation of the common eigenvectors using the Flury-Gautschi (or other) algorithm, the off-diagonal elements of the nearly diagonalised covariance matrices are shrunk towards zero and multiplied with the orthogonal common eigenvector matrix to obtain the regularised CPC covariance matrix ...
Analysing the distance matrix using Principal Component Analysis (PCA) would satisfy this criterion because it does not assume a specific structure of data (Fig. 1, conventional PCA). Rather, it rotates the matrix and projects it to sets of diagonal axes; it finds directions of differences and...
Note that the percentage of variance explained is smaller than with PCA: FA concentrates on explaining the off-diagonal elements of the covariance matrix, whereas PCA optimizes the explained variance, that is, the diagonal elements. However, the results are very similar to PCA, notwithstanding the...
We propose a new approach to address the problem of PCA on geographical data that poses it as a location-scale model where the mean and the covariance matrix elements are calculated by means of generalized additive models (GAMs). GAM is a flexible, powerful and highly interpretable tool (Hasti...
is the diagonal elements of matrix S. Choose the minimal value k when the above equation satisfies. Q:Why choose the first k dimensions with the greatest variance. A:The underlying assumption is thatlarge variances have important dynamics. Hence, principal components with larger associated variances...