In computer sciences, matrices are widely used for representing different kinds of information. Measuring similarity among square matrices is an interesting open problem in computer sciences. Furthermore, eigenvalues and eigenvectors are a powerful way for representing and characterizing square matrices. In this paper we introduce a new similarity measure among two square data matrices of the same class; the idea is based on evaluating the effect of conjugate the eigenvalues and eigenvectors of one matrix with the other matrix, and vice versa. Some experimental results are showed in order to analyze and exemplify the Eigenconjugation as an approach for the problem of similarity of matrices.

We introduce a method for computing similarity between two square matrices based on the information given by their eigenvalues and eigenvectors. The idea is to evaluate the effect of the conjugation of the original matrices and the eigenvectors and eigenvalues of each other. Then, we exemplify its utility for computing similarity between square images from a classified bank of pictures. The performance of the method is evaluated with diverse experiments.

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