I am using two different models, Latent Dirichlet Allocation (LDA) and WordVec, to create feature vectors for document classification. The output of the LDA model is a probability vector, i.e. the components are all non-negative and sum to 1. The vectors derived from the WordVec algorithm are not probabilities, i.e. they are not non-negative and do not sum to 1. I am wondering how I can combine these two vectors. One thought I have is to softmax the WordVec vectors and then do the combination (which will be a concatenation). Otherwise, I'm not sure how to properly normalize the concatenated vectors.