Entropic Graph-based Posterior Regularization.

ABSTRACT: Graph smoothness objectives have achieved great success in semi-supervised learning but have not yet been applied extensively to unsupervised generative models. We define a new class of entropic graph-based posterior regularizers that augment a probabilistic model by encouraging pairs of nearby variables in a regularization graph to have similar posterior distributions. We present a three-way alternating optimization algorithm with closed-form updates for performing inference on this joint model and learning its parameters. This method admits updates linear in the degree of the regularization graph, exhibits monotone convergence, and is easily parallelizable. We are motivated by applications in computational biology in which temporal models such as hidden Markov models are used to learn a

SUBMITTER: Libbrecht MW 

PROVIDER: S-EPMC11526501 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature