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Efficient Learning Algorithm for Maximum Entropy Discrimination Topic Models
Pages: 736-745
Year: Issue:  8
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  Supervised Topic ModelsCoordinate DescentGibbs SamplingRejection Sampling;
Abstract: Time complexity of the existing supervised topic model training algorithms is generally linear to the number of topics and therefore their large-scale application is limited. To solve this problem, an efficient learning algorithm for maximum entropy discrimination of latent Dirichlet allocation(MedLDA) supervised subject model is proposed in this paper. The proposed algorithm is based on coordinate descent, and the number of iterations of training classifiers is less than that of the existing Monte Carlo algorithm for MedLDA. The algorithm also makes use of rejection sampling and efficient preprocessing technique to reduce the time complexity of training from linear to sub-linear with respect to the number of topics. The comparison experiments on multiple text corpora show that the proposed algorithm makes a great improvement in training speed compared with the existing Monte Carlo algorithm.
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