Title :
A Non-parametric Approach to Pair-Wise Dynamic Topic Correlation Detection
Author :
Song, Yang ; Zhang, Lu ; Giles, C. Lee
Author_Institution :
Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA
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, and (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.
Keywords :
Gaussian processes; document handling; document corpora; hierarchical Gaussian process latent variable models; nonlinear dimension reduction technique; nonparametric approach; pairwise dynamic topic correlation detection; topic correlations; topic evolution; Computer science; Data engineering; Data mining; Gaussian distribution; Gaussian processes; Linear discriminant analysis; Logistics; Parameter estimation; Predictive models; Statistics; Gaussian processes; correlation analysis; topic models;
Conference_Titel :
Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on
Conference_Location :
Pisa
Print_ISBN :
978-0-7695-3502-9
DOI :
10.1109/ICDM.2008.20