On the equivalence between algorithms for Non negative Matrix Factorization and Latent Dirichlet Allocation
Abstract. LDA (Latent Dirichlet Allocation and NMF (Non negative Matrix Factorization) are two popular techniques to extract topics in a textual document corpus. This paper shows that NMF with KullbackLeibler divergence approximate the LDA model under a uniform Dirichlet prior, therefore the comparative analysis can be useful to elucidate the implementation of variational inference algorithm for LDA.
ESANN