Extensions
- Hierarchical extensions:
- Asymmetric: MASHA ("Multinomial ASymmetric Hierarchical Analysis")
- Symmetric: HPLSA ("Hierarchical Probabilistic Latent Semantic Analysis")
- Generative models: The following models have been developed to address an often-criticized shortcoming of PLSA, namely that it is not a proper generative model for new documents.
- Latent Dirichlet allocation - adds a Dirichlet prior on the per-document topic distribution
- Higher-order data: Although this is rarely discussed in the scientific literature, PLSA extends naturally to higher order data (three modes and higher), i.e. it can model co-occurrences over three or more variables. In the symmetric formulation above, this is done simply by adding conditional probability distributions for these additional variables. This is the probabilistic analogue to non-negative tensor factorisation.
Read more about this topic: Probabilistic Latent Semantic Analysis
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