Concentrated Document Topic Model
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Abstract (arXiv)
We propose a Concentrated Document Topic Model(CDTM) for unsupervised text classification, which is able to produce a concentrated and sparse document topic distribution. In particular, an exponential entropy penalty is imposed on the document topic distribution. Documents that have diverse topic distributions are penalized more, while those having concentrated topics are penalized less. We apply the model to the benchmark NIPS dataset and observe more coherent topics and more concentrated and sparse document-topic distributions than Latent Dirichlet Allocation(LDA).
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References · 6
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2016Variational Inference: A Review for Statisticianscited by 1,472
- 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464
- 2018Distilled Wasserstein Learning for Word Embedding and Topic Modelingcited by 18
- 2016Learning Online Alignments with Continuous Rewards Policy Gradientcited by 6
- 2017Jointly Learning Word Embeddings and Latent Topicscited by 6
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