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dc.contributor.advisorRavikumar, Pradeepen
dc.contributor.committeeMemberDhillon, Inderjiten
dc.creatorJohnson, Christopher Carrollen
dc.date.accessioned2011-11-04T16:54:37Zen
dc.date.accessioned2017-05-11T22:23:40Z
dc.date.available2011-11-04T16:54:37Zen
dc.date.available2017-05-11T22:23:40Z
dc.date.issued2011-08en
dc.date.submittedAugust 2011en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2011-08-4331en
dc.descriptiontexten
dc.description.abstractProbabilistic graphical models are used in a variety of domains to capture and represent general dependencies in joint probability distributions. In this document we examine the problem of learning the structure of an undirected graphical model, also called a Markov Random Field (MRF), given a set of independent and identically distributed (i.i.d.) samples. Specifically, we introduce an adaptive forward-backward greedy algorithm for learning the structure of a discrete, pairwise MRF given a high dimensional set of i.i.d. samples. The algorithm works by greedily estimating the neighborhood of each node independently through a series of forward and backward steps. By imposing a restricted strong convexity condition on the structure of the learned graph we show that the structure can be fully learned with high probability given $n=\Omega(d\log (p))$ samples where $d$ is the dimension of the graph and $p$ is the number of nodes. This is a significant improvement over existing convex-optimization based algorithms that require a sample complexity of $n=\Omega(d^2\log(p))$ and a stronger irrepresentability condition. We further support these claims with an empirical comparison of the greedy algorithm to node-wise $\ell_1$-regularized logistic regression as well as provide a real data analysis of the greedy algorithm using the Audioscrobbler music listener dataset. The results of this document provide an additional representation of work submitted by A. Jalali, C. Johnson, and P. Ravikumar to NIPS 2011.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectMachine learningen
dc.subjectGraphical modelsen
dc.subjectMarkov Random Fieldsen
dc.subjectStructure learningen
dc.subjectProbabilityen
dc.subjectUncertaintyen
dc.subjectGreedy algorithmsen
dc.titleGreedy structure learning of Markov Random Fieldsen
dc.description.departmentComputer Sciencesen
dc.type.genrethesisen
dc.date.updated2011-11-04T16:54:45Zen
dc.identifier.slug2152/ETD-UT-2011-08-4331en


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