Speaker
Description
Symmetric tensor diagonalization has applications in statistics and signal processing. Unlike for real symmetric matrices, there is no guarantee that a real-valued symmetric tensor is diagonalizable. Therefore, one generally approaches the problem as an approximate tensor diagonalization (ATD) problem. In this talk, we show that Jacobi-type methods for ATD that naturally extend the Jacobi method for real symmetric matrix diagonalization share several useful properties with the original method, including the conservation of mass during the iteration steps. We use these properties to generalize the greedy method for the matrix case to third-order real-valued tensors, and present numerical results comparing our approach to the classical sweep method. We discuss convergence properties and place our work in the context of Jacobi-type approaches for ATD.