Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among ...

Graph Theory Matrix Approach (GTMA) is moving with great speed into the main stream of computer design, Information sciences, Information and Computer programming, Artificial Intelligence and design, ...

Analyzing large data sets are challenging. Most data analytics research has proposed parallel algorithms that outside a DBMS because SQL is considered inadequate for complexity computations. R and ...

Typically, observations/samples are from several heterogenous groups and the group membership of each observation/sample is unavailable, which poses a great challenge for graph structure learning. In ...

Resulting Matrix. This produces a new matrix whose order is equal to the number of rows of the first matrix by the number of columns of the second matrix. This is known as the matrix product or the ...

Quantum graphs—networks composed of vertices connected by edges on which quantum wave dynamics are defined—have emerged as a versatile model for exploring the interplay between geometry ...

Keywords: non-negative matrix factorization, graph Laplacian regularization, drug-miRNA associations, weighted k nearest neighbor, sparse similarity matrix Citation: Wang M-N, Li Y, Lei L-L, Ding D-W ...