of Matrices#The Companion Matrix#Splitting to Invariant Subspaces#An Upper Triangular Form#Jordan Canonical Form#Some Applications of Jordan Canonical Form#The Matrix EquationAXXB= 0#A Criterion for Similarity of Two Matrices#The Matrix EquationAXXB= C#A Case of Two Nilpotent Matrices#Historical ...
10.The necessary and sufficient conditions ofsimilarityof two idempotent matrices are proved, the idempotency of linear combinations of two idempotent matrices is discussed. 摘要证明了数域上两个同阶幂等阵似 充要条件是它们有 同 秩;给出了幂等阵 ...
a transition matrix is a data sheet containing hundreds or even thousands of numbers and very difficult to be fully appreciated even for an expert eye. For this reason, transition matrices are normally elaborated
A simple algorithm is presented for testing the diagonal similarity of two square matrices with entries in a field. Extended forms of the algorithm decide various related problems such as the simultaneous diagonal similarity of two families of matrices, the existence of a matrix in a subfield diago...
Evaluating unsupervised classifiers with similarity and comparison matricesSYNTHETIC APERTURE RADAR (SARRADIOMETRIC CORRECTIONSMONOPULSETOPOGRAPHIC MAPPINGIn this work, the evaluation of the unsupervised classification by the matrix with the measures of similarity is being presented. This matrix has been created...
Guralnick, R.M.: A note on the local-global principle for similarity of matrices. Linear Algebra Appl. 30, 241-245 (1980)R. M. Guralnick, A note on the local-global principle of similarity of matrices, Linear Algebra Appl. 30:241-245 (1980)....
[0052] It can be understood that the general result of the similarity operation of the present invention applied to two matrices (or a matrix D and a vector Yin, as per equation 3 above) is a matrix (or vector) wherein the element of the ith row and jth column is determined from the...
The superiority of NDD performance on integrated similarity over its performance when provided with each of similarities, completely verify that making use of integrated similarity matrices leads to discover more discriminant features and significantly improves the prediction performance. In addition, we ...
Often, the methods rely on simple correlation between the similarity matrices learned by each model, but newer generalized methods (e.g., Hu, Cai, Graesser, & Ventura, 2005) allow for the comparison of spaces that differ in metric (e.g., comparing an LSA space to a probabilistic topic ...
Such a loss function can be constructed by using some measures of distance between two matrices A and A *. For example, the cost function with the square of the Euclidean distance can be written as $${\boldsymbol{l}}({A}_{ij},{A}_{ij}^{\ast }(\theta ))=||A-{A}^{\ast }...