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I understand that the output for Latent Dirichlet Allocation is a distribution over K topics. If I had a Dx(K+1) matrix, where rows are documents and columns are the topic distribution + one column for class, how would I go about creating a Naive Bayes Classifier? Typically, I have used a Gaussian Naive Bayes where the feature space is iid over normally distributed variables, but this assumption I do not believe makes sense for LDA output. Would I need to assume the features are the individual columns distributed in a particular way? |
