A non-parametric approach to pair-wise dynamic topic correlation detection

Song Yang, Zhang Lu, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce dynamic correlated topic models (DCTM) for analyzing discrete data over time. This model is inspired by the hierarchical Gaussian process latent variable models (GP-LVM). DCTM is essentially a non-linear dimension reduction technique which is capable of (1) detecting topic evolution within a document corpus, (2) discovering topic correlations between document corpora, (3) monitoring topic and correlation trends dynamically. Unlike generative aspect models such like LDA, DCTM demonstrates a much faster converging rate with better model fitting to the data. We empirically assess our approach using 268,231 scientific documents, from the year 1988 to 2005. Posterior inferences suggest that DCTM is useful for capturing topic and correlation dynamics, as well as predicting their trends.

Original languageEnglish (US)
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages1031-1036
Number of pages6
DOIs
StatePublished - Dec 1 2008
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other8th IEEE International Conference on Data Mining, ICDM 2008
CountryItaly
CityPisa
Period12/15/0812/19/08

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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