Structure Learning Using Forced Pruning
This is an incremental improvement for applications in natural language processing, computer vision, and bioinformatics.
The authors tackled the problem of learning Markov network structures by proposing a computationally tractable greedy heuristic that limits the number of parameters, and they found it performs comparably to state-of-the-art methods on three real datasets.
Markov networks are widely used in many Machine Learning applications including natural language processing, computer vision, and bioinformatics . Learning Markov networks have many complications ranging from intractable computations involved to the possibility of learning a model with a huge number of parameters. In this report, we provide a computationally tractable greedy heuristic for learning Markov networks structure. The proposed heuristic results in a model with a limited predefined number of parameters. We ran our method on 3 fully-observed real datasets, and we observed that our method is doing comparably good to the state of the art methods.