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In the last article, I explained LDA parameter inference using variational EM algorithm and implemented it from scratch. In this post, let’s take a look at another algorithm proposed in the original paper that introduced LDA to derive approximate posterior distribution: Gibbs sampling. In addition, I would like to introduce and implement from scratch a collapsed Gibbs sampling method that can efficiently fit topic model to the data.
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Now that we know the structure of the model, it is time to fit the model parameters with real data. Among the possible inference methods, in this article I would like to explain the variational expectation-maximization algorithm.
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In the last article, topic models frequently used at the time of development of LDA was covered. At the end of the post, I briefly introduced the rationale behind LDA. In this post, I would like to elaborate on details of the model architecture.
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Latent Dirichlet allocation (LDA) is a three-level bayesian hierarchical model that is frequently used for topic modelling and document classification. First proposed to infer population structure from genotype data, LDA not only allows to represent words as mixtures of topics, but to represent documents as a mixture of words, which makes it a powerful generative probabilistic model.
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While previous word embedding models focused on word-level features such as n-gram, FastText additionally focused on character-level features (subwords) to add flexibility to the model.
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