Asymptotic analysis of a matrix latent decomposition model

Matrix data sets arise in network analysis for medical applications, where each belongs to a subject and represents measurable phenotype. These large dimensional are often modeled using lower-dimensional latent variables, which explain most of the observed variability can be used predictive purposes. In this paper, we provide asymptotic convergence guarantees estimation hierarchical statistical model matrix sets. It captures matrices by modeling truncation their eigendecomposition. We show that is identifiable, consistent Maximum A Posteriori (MAP) performed estimate distribution eigenvalues eigenvectors. The MAP estimator shown asymptotically normal restricted version model.