Exclusive Topic Modeling
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Abstract (arXiv)
We propose an Exclusive Topic Modeling (ETM) for unsupervised text classification, which is able to 1) identify the field-specific keywords though less frequently appeared and 2) deliver well-structured topics with exclusive words. In particular, a weighted Lasso penalty is imposed to reduce the dominance of the frequently appearing yet less relevant words automatically, and a pairwise Kullback-Leibler divergence penalty is used to implement topics separation. Simulation studies demonstrate that the ETM detects the field-specific keywords, while LDA fails. When applying to the benchmark NIPS dataset, the topic coherence score on average improves by 22% and 10% for the model with weighted Lasso penalty and pairwise Kullback-Leibler divergence penalty, respectively.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 6 Feb 2021.
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9 of 43 references.
References · 9
- 2016Variational Inference: A Review for Statisticianscited by 1,466
- 2017Frequentist Consistency of Variational Bayescited by 60
- 2017Convergence Rates of Variational Posterior Distributionscited by 45
- 2017Neon2: Finding Local Minima via First-Order Oraclescited by 36
- 2015Neural network-based clustering using pairwise constraintscited by 22
- 2017On Statistical Optimality of Variational Bayescited by 21
- 2018Distilled Wasserstein Learning for Word Embedding and Topic Modelingcited by 18
- 2014How Many Topics? Stability Analysis for Topic Modelscited by 16
- 2017Jointly Learning Word Embeddings and Latent Topicscited by 6
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