Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary.
|Original language||English (US)|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - Jan 1 2013|
|Event||27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States|
Duration: Dec 5 2013 → Dec 10 2013