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Poster
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 #None

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 (Duke University)

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