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タイトル
  • A Revised Inference for Correlated Topic Model
作成者
    • Masada, Tomonari
    • Takasu, Atsuhiro
権利情報
  • © 2013 Springer-Verlag Berlin Heidelberg.
主題
  • Other covariance matrix
  • Other Topic models
  • Other variational inference
内容注記
  • Abstract 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.
  • Other 10th International Symposium on Neural Networks, ISNN 2013; Dalian; China; 4 July 2013 through 6 July 2013
  • Other Lecture Notes in Computer Science, 7952, pp.445-454; 2013
出版者 Springer Verlag
日付
    Issued2013-07
言語
  • eng
資源タイプ conference paper
出版タイプ AM
資源識別子 HDL http://hdl.handle.net/10069/33725 , URI https://nagasaki-u.repo.nii.ac.jp/records/6724
関連
  • isVersionOf DOI https://doi.org/10.1007/978-3-642-39068-5_54
収録誌情報
    • ISSN 03029743
    • ISSN 16113349
      • Lecture Notes in Computer Science
      • 巻7952 開始ページ445 終了ページ454
ファイル
コンテンツ更新日時 2023-07-06