Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields
This work addresses scalability issues in structure learning for MRFs, offering an incremental improvement for researchers and practitioners dealing with large-scale probabilistic graphical models.
The paper tackles the computational bottlenecks in incremental structure learning of Markov Random Fields by introducing best-choice edge grafting, which activates edges as groups in a streaming setting, resulting in significant speedup and a controllable trade-off between speed and learning quality.
Incremental methods for structure learning of pairwise Markov random fields (MRFs), such as grafting, improve scalability by avoiding inference over the entire feature space in each optimization step. Instead, inference is performed over an incrementally grown active set of features. In this paper, we address key computational bottlenecks that current incremental techniques still suffer by introducing best-choice edge grafting, an incremental, structured method that activates edges as groups of features in a streaming setting. The method uses a reservoir of edges that satisfy an activation condition, approximating the search for the optimal edge to activate. It also reorganizes the search space using search-history and structure heuristics. Experiments show a significant speedup for structure learning and a controllable trade-off between the speed and quality of learning.