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Dynamic Rank Factor Model for Text Streams
Shaobo Han · Lin Du · Esther Salazar · Lawrence Carin

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D

We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (1) discovering topic prevalence over time, and (2) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backward-sampling algorithm. Moreover, an efficient data subsampling scheme is leveraged to speed up inference on massive datasets. The modeling framework is illustrated on two real datasets: the US State of the Union Address and the JSTOR collection from Science.

Author Information

Shaobo Han (Duke University)
Lin Du (Duke University)
Esther Salazar (Duke University)
Lawrence Carin (KAUST)

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