Latent Dirichlet Allocation (LDA) is a generative model which produces a list of topics. Each topic is represented by a distribution over words.


  • What is the objective function that it tries to minimize? Can someone explain it in simpler way? The mathematical formula and the intuition behind it?

  • Secondly, After building the LDA model how we validate the model? I have heard perplexity as one metric, but what is perplexity about?

  • Does bias, Variance problem also exists in LDA? If yes, when it can occur?



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