Parallel Loopy Belief Propagation in Conditional Random Fields
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Date
2012-02-21
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Abstract
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.