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Statistical dependencies observed in real-world phenomena often change drastically with time. Graphical dependency models, such as the Markov networks (MNs), must deal with this temporal heterogeneity in order to draw meaningful conclusions about the transient nature of the target phenomena. However, in practice, the estimation of time-varying dependency graphs can be inefficient due to the potentially large number of parameters of interest. To overcome this problem, we propose such a novel approach to learning time-varying MNs that effectively reduces the number of parameters by constraining the rank of the parameter matrix. The underlying idea is that the effective dimensionality of the parameter space is relatively low in many realistic situations. Temporal smoothness and sparsity of the network are also incorporated as in previous studies. The proposed method is formulated as a convex minimization of a smoothed empirical loss with both the $$\\ell _1$$ℓ1- and the nuclear-norm regularization terms. This non-smooth optimization problem is numerically solved by the alternating direction method of multipliers. We take the Ising model as a fundamental example of an MN, and we show in several simulation studies that the rank-reducing effect from the nuclear norm can improve the estimation performance of time-varying dependency graphs. We also demonstrate the utility of the method for analyzing real-world datasets for improving the interpretability and predictability of the obtained networks.

Sparse and low-rank matrix regularization for learning time-varying Markov networks