@inproceedings{oai:nagasaki-u.repo.nii.ac.jp:00006724,
 author = {Masada, Tomonari and Takasu, Atsuhiro},
 book = {Lecture Notes in Computer Science},
 month = {Jul},
 note = {In this paper, we provide a revised inference for correlated topic model (CTM) [3]. CTM is proposed by Blei et al. for modeling correlations among latent topics more expressively than latent Dirichlet allocation (LDA) [2] and has been attracting attention of researchers. However, we have found that the variational inference of the original paper is unstable due to almost-singularity of the covariance matrix when the number of topics is large. This means that we may be reluctant to use CTM for analyzing a large document set, which may cover a rich diversity of topics. Therefore, we revise the inference and improve its quality. First, we modify the formula for updating the covariance matrix in a manner that enables us to recover the original inference by adjusting a parameter. Second, we regularize posterior parameters for reducing a side effect caused by the formula modification. While our method is based on a heuristic intuition, an experiment conducted on large document sets showed that it worked effectively in terms of perplexity., 10th International Symposium on Neural Networks, ISNN 2013; Dalian; China; 4 July 2013 through 6 July 2013, Lecture Notes in Computer Science, 7952, pp.445-454; 2013},
 pages = {445--454},
 publisher = {Springer Verlag},
 title = {A Revised Inference for Correlated Topic Model},
 volume = {7952},
 year = {2013}
}