Papers with matching high dimensional Dirichlet distribution
Topic Modeling with Wasserstein Autoencoders (P19-1)
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| Challenge: | Existing probabilistic topic models are based on latent Dirichlet allocations and collapsed Gibbs sampling. |
| Approach: | They propose a novel topic model that enforces Dirichlet prior on latent document-topic vectors and a kernel kernel to minimize the Maximum Mean Discrepancy (MMD) They propose to measure the diversity of the produced topics and to use the widely used coherence measure NPMI to evaluate topic quality. |
| Outcome: | The proposed model performs better than existing topic models on real datasets. |