In the MUSIC approach for multiple emitter location, the array covariance matrix is a Hermite matrix. In order to realize the MUSIC approach, we have to do the work of eigenvalue-based decomposition of the Hermite matrix. This paper proves that the problem of Hermite matrix decomposition can be transformed into the problem of real symmetric matrix decomposition, and the article gives the detail...

The undirected power graph of a finite group $G$, $P(G)$, is a graph with the group elements of $G$ as vertices and two vertices are adjacent if and only if one of them is a power of the other. Let $A$ be an adjacency matrix of $P(G)$. An eigenvalue $lambda$ of $A$ is a main eigenvalue if the eigenspace $epsilon(lambda)$ has an eigenvector $X$ such that $X^{t}jjneq 0$, where $jj$ is the all-one...

We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, first-order perturbation effects, etc., are analyzed. We investigate how tensor symmetries affect the decomposition and propose a multilinear generaliz...

Presented are two related numerical methods, one for the inverse eigenvalue problem for nonnegative or stochastic matrices and another for the inverse eigenvalue problem for symmetric nonnegative matrices. The methods are iterative in nature and utilize alternating projection ideas. For the symmetric problem, the main computational component of each iteration is an eigenvalue-eigenvector decomp...

Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple and could require user to model super-dyadic associations Such modeled using a hypergraph, which generalization of graph where hyperedge connect In this work, we consider problem k-uniform hypergraph. We utilize tensor-based representation hypergraphs propo...