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Incremental methods for structure learning of pairwise Markov random fields(MRFs), such as grafting, improve scalability by avoiding inference over theentire feature space in each optimization step. Instead, inference is performedover an incrementally grown active set of features. In this paper, we addresskey computational bottlenecks that current incremental techniques still sufferby introducing best-choice edge grafting, an incremental, structured methodthat activates edges as groups of features in a streaming setting. The methoduses a reservoir of edges that satisfy an activation condition, approximatingthe search for the optimal edge to activate. It also reorganizes the searchspace using search-history and structure heuristics. Experiments show asignificant speedup for structure learning and a controllable trade-off betweenthe speed and quality of learning.

Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields