Conditional Random Field (CRF) Toolbox for Matlab

This toolbox supports inference and learning for CRFs, with the following topologies: chains, 2D lattices, arbitrary graphs. It assumes all potentials are pairwise. Inference is done using belief propagation (my BPMRF2 and BPlattice code). For chains and trees, this is exact, otherwise it is approximate.
See also 1D CRFs in Java

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数据挖掘研究院

Download

This code contains netlab 3.3 bundled in. 数据挖掘研究院

Installation

>> addpath(genpath(′C:/CRFall′))
>> testCRF
 
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Documentation

Represenation

• crfchain for 1D chains (in CRF1D)
• crf2Ddemo for 2D lattices (in CRF2D)
• crf for general graphs (in CRF)

All models have potentials defined as follows:

• For local evidence for node i in state qi:

    phi{i}(qi) = exp[ sum_d w{i}(d,qi) f{i}(d) ]
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  where f{i}(d) is the d′th element of the feature vector for node i and w{i}(d, qi) is the weight matrix representing the compatibility of feature d to state qi.

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• For hidden edge e conecting nodes i and j in states qi, qj:

     psi{e}(qi,qj) is a table.
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  Note that the current implementation does not support data-dependent edge potnetials. Also, the edge potentials take O(K^2) space to represent, although they can be tied.

Inference

• BP (belief propagation), which is exact for chains and trees
• Exact-2D (by converting the 2D lattice to an HMM - this only works for small grids)
• Graph cuts: to be added.

Learning

• Various first order gradient methods : see crf2Ddemo for a list. You can extend this by modifying the switch statement in crf2DdemoTrain.
• Pseudo-likelihood: to be added.

• crfpack to extract all the parameters from the CRF structure into an initial parameter vector.
• crfunpack to store the final parameter vector inside the CRF structure.
• Some function to compute the gradient such as crfErrAndGrad. The ′net′ crf structure contains the name of the inference engine internally.
• Some gradient based optimizer (eg fminsearch).

Further reading

• Hanna Wallach′s CRF reading list