In a paper being presented in December at the annual conference of the Neural Information Processing Systems Foundation, MIT researchers describe a new technique that expands the class of data sets whose structure can be efficiently deduced. Not only that, but their technique naturally describes the data in a way that makes it much easier to work with. In technical terms, the researchers’ work concerns probabilistic graphical models. Historically, graphical models have sped up machine-learning algorithms only when they’ve had a few particular shapes, such as that of a tree. A tree is a graph with no closed loops: In a family tree, for instance, a closed loop would indicate something biologically impossible — that, say, someone is both parent and sibling to the same person.
According to Ying Liu, a graduate student in MIT’s Department of Electrical Engineering and Computer Science who co-wrote the new paper with his advisor, Alan Willsky, the Edwin Sibley Webster Professor of Electrical Engineering, loops pose problems because they can make statistical inference algorithms “overconfident.” The algorithm typically used to infer statistical relationships within graphical models, Liu explains, is a “message-passing algorithm, where each node sends messages to only its neighbors, using only local information and incoming messages from other neighbors. It’s a very good way to distribute the computation.”
Continue reading the article on MIT News.