PLSA assumes each document has a distribution over K topics P (z k |d j ), and each topic has a distribution over all vocabularies P (w i |z k ).
pLSA finds a topic distribution for each concept definition that maximizes the log likelihood of the corpus X (LDA has a similar form): In this formulation, missing words do not contribute to the estimation of sentence semantics, i.e., excluding missing words (X ij = 0) in equation 1 does not make a difference.