Our LF-DMM model uses the DMM model assumption that all the words in a document share the same topic.
the process of generating a document in a document collection with our LF-DMM is as follows: a distribution over topics θ is drawn for the document collection; then the model draws a topic indicator z d for the entire document d; for every i th word w d i in the document d, a binary indicator variable s d i is sampled from a Bernoulli distribution to determine whether the Dirichlet multinomial or latent feature component will be used to generate the word w d i , and finally the word is generated from the same topic z d by the determined component.