Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Mathematical Programming: Series A and B
Modeling changing dependency structure in multivariate time series
Proceedings of the 24th international conference on Machine learning
The Journal of Machine Learning Research
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods
The Journal of Machine Learning Research
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Restoration of Poissonian images using alternating direction optimization
IEEE Transactions on Image Processing
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Several authors have recently proposed sparse estimation techniques for time-varying Markov networks, in which both graph structures and model parameters may change with time. In this study, we extend a previous approach with a low-rank assumption on the matrix of parameter sequence, utilizing a recent technique of nuclear norm regularization. This can potentially improve the estimation performance particularly in such cases that the local smoothness assumed in previous studies do not really hold. Then, we derive a simple algorithm based on the alternating direction method of multipliers (ADMM) which can effectively utilize the separable structure of our convex minimization problem. With an artificially-generated dataset, its superior performance in structure learning is demonstrated.