Computing the inverse of a matrix is one of the most important operations in machine learning. If some matrix A has shape n-by-n, then its inverse matrix Ai is n-by-n and the matrix product of Ai * A ...

THE problem of ‘inverting’ singular matrices is by no means uncommon in statistical analysis. Rao 1 has shown in a lemma that a generalized inverse (g-inverse) always exists, although in the case of a ...

In this paper, we study the matrix denoising model Y = S + X, where S is a low rank deterministic signal matrix and X is a random noise matrix, and both are M × n. In the scenario that M and n are ...

where square-matrix is a numeric matrix or literal. The DET function computes the determinant of square-matrix, which must be square. The determinant, the product of the eigenvalues, is a single ...