On smoothing and inference for topic models
Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years, including collapsed Gibbs sampling, variational inference, and maximum a posteriori estimation, and...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2009
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