I understand that the output for Latent Dirichlet Allocation is a distribution over K topics.
Suppose I have a Dx(K+1) matrix, where rows are documents and columns are the topic distribution + one column for class. For example, each row represents one movie review. The first K columns represent the topic distribution of the document, and the last column is the classification of this document. For example, if K=5, one row may read as:
2 | 0.25 | 0.4 | 0.1 | 0.15 | 0.1 | 1
where moview review 2 had 25% of the text about topic 1 (pleasure), 40% topic 2 (discontent), 10% topic 3 (personal feelings) ... and the classification of this document was to class #1.
How would I go about creating a Naive Bayes Classifier using this data?
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 (say a Dirichlet probability)?
This exercise is more for proof of concept to use Naive Bayes. I want to use, say 60%, of the data to construct a Naive Bayes Classifier and test the accuracy of the remaining 40%. My main concern is to how to define the PDF for the Naive Bayes Classifier.