Proceedings Abstracts of the Twenty-Fourth International Joint Conference on Artificial Intelligence

Data Compression for Learning MRF Parameters / 3784
Khaled S. Refaat, Adnan Darwiche

We propose a technique for decomposing and compressing the dataset in the parameter learning problem in Markov random fields. Our technique applies to incomplete datasets and exploits variables that are always observed in the given dataset. We show that our technique allows exact computation of the gradient and the likelihood, and can lead to orders-of-magnitude savings in learning time.