NDLI: A Non-parametric Approach to Pair-Wise Dynamic Topic Correlation Detection

Content Provider	IEEE Xplore Digital Library
Author	Yang Song Lu Zhang Giles, C.L.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA (Yang Song)
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.
Starting Page	1031
Ending Page	1036
File Size	263946
Page Count	6
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear discriminant analysis Predictive models Gaussian distribution Gaussian processes Parameter estimation Logistics Data mining Computer science Data engineering Statistics correlation analysis topic models
Content Type	Text
Resource Type	Article

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