I'm comparing various topic modeling algorithms on a data set and I'm hoping to compare them via looking at the log-likelihood of held out documents. I'm using SKLearn and I can see that PCA allows for a log-likelihood score (it uses the interpretation in this paper). However, PCA doesn't allow me to easily figure out what the topics are.

In contrast, with LSA (via SVD) I can easily extract the topics by looking at the columns of U where $$ A= U\Sigma V^T $$ So I'm wondering if there is a way to either: (1) go back and forth between the two; (2) extract the topics from PCA; or (3) get a log-likelihood of new documents after SVD?

I looked for answers here and I saw this: but I'm not able to use it derive an equation to go from the PCA to the SVD or vice-versa.

Alternatively, could I just calcuatle the SVD and PCA of the matrix and use the SVD to get the features and PCA to get the log-likelihood (i.e. would they correspond to each other)?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.