Variational inference for conditional random fields

Conditional random fields (CRFs) have been popular for contextual pattern classification. This paper presents two variational inference methods for direct approximation of a conditional probability instead of indirect calculation through Viterbi approximation of a marginal probability. The CRFs with the factorized variational inference (FVI) and the structured variational inference (SVI) are proposed and investigated for human motion recognition. In general, FVI assumes a factorization of variational distributions of individual states for representation of conditional probability while SVI preserves the state structure in the variational distribution. In the experiments on using IDIAP human motion database, we found that CRFs using variation inference methods performed better than baseline CRFs using Viterbi approximation. CRFs with SVI obtained higher classification accuracy than those with FVI.