The {\em tensor power method} generalizes the matrix method to higher order arrays, or tensors. Like in case, fixed points of are eigenvectors tensor. While every real symmetric has an eigendecomposition, vectors generating a decomposition not always In this paper we show that whenever eigenvector is} generator tensor, then (if is sufficiently high) robust} , i.e., it attracting point method....

We analyze the eigenvectors of generalized Laplacian for two metric graphs occurring in practical applications. In accordance with random network theory, localization an eigenvector is rare and should be tuned to observe exactly localized eigenvectors. derive resonance conditions obtain various geometric configurations their combinations form more complicated resonant structures. These suggest ...

in this work, using eigenvalues and eigenvectors of unitary cayley graphs over finite local rings and elementary linear algebra, we characterize which local rings allowing pst occurring in its unitary cayley graph. moreover, we have some developments when $r$ is a product of local rings.

A method for object recognition and pose estimation for robotic bin picking is presented. The approach discussed is a variant on current approaches to eigenimage analysis. Compared to traditional approaches which use object geometry only (shape invariants), the implementation described uses the eigenspace determined by processing the eigenvalues and eigenvectors of the image set. The image set ...

We show that averaging eigenvectors of randomly sampled submatrices efficiently approximates the true eigenvectors of the original matrix under certain conditions on the incoherence of the spectral decomposition. This incoherence assumption is typically milder than those made in matrix completion and allows eigenvectors to be sparse. We discuss applications to spectral methods in dimensionality...