A EM-algorithm to fit LDF A-H (Section 2) Initialization Let null θ

Since the MPLE objective function for LDFA-H given in Eq. (9) is not guaranteed convex, an EM-algorithm may find a local minimum according to a choice of the initial value. Hence a good initialization is crucial to a successful estimation. According to the equivalence between CCA and probablistic CCA shown by A. Anonymous, it gives (r 1) (r 1) (r 1) (r 1) Lasso problem is solved by the P-GLASSO algorithm by Mazumder et al. (2010). We simulated realistic data with known cross-region connectivity as follows. Notice that the amplitudes of the top four factors dominate the others.