Parallel Loopy Belief Propagation in Conditional Random Fields
Structured real world data can be represented with graphs whose
structure encodes indepen dence assumptions within the data. Due to
statistical advantages over generative graphical models, Conditional
Random Fields (CRFs) are used in a wide range of classification tasks on
structured data sets. CRFs can be learned from both, fully or partially
supervised data, and may be used to infer fully unlabeled or partially
labelled data. However, performing inference in CRFs with an arbitrary
graphical structure on a large amount of data is computational expensive
and nearly intractable on a reseacher’s workstation. Hence, we take
advantage of recent developments in computer hardware, namely
general-purpose Graphics Processing Units (GPUs). We not merely run
given algorithms on GPUs, but present a novel framework of parallel
algorithms at several levels for training general CRFs on very large
data sets. We evaluate their performance in terms of runtime and
F1-Score.