- LDA stands for Latent Dirichlet Allocation, and it is a type of topic modeling algorithm
- LDA was developed in 2003 by researchers David Blei, Andrew Ng and Michael Jordan
- LDA is based on a Bayesian framework. This allows the model to infer topics based on observed data (words) through the use of conditional probabilities
- The main difference between LSA and LDA is that LDA assumes that the distribution of topics in a document and the distribution of words in topics are Dirichlet distributions. LSA does not assume any distribution and therefore, leads to more opaque vector representations of topics and documents
- Latent Semantic Analysis or Latent Semantic Indexing – Uses Singular Value Decomposition (SVD) on the Document-Term Matrix
- In practice, LSA is much faster to train than LDA, but has lower accuracy.
Example
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