For each latent cluster m: where Ga(•, •) is the gamma distribution with the shape and rate parameters; The main idea of MIGA is the meta-info guided aggregation, where instead of putting an uninformative prior on ψ d , MIGA constructs an informative document-specific Dirichlet prior with parameter π d computed from the document's labels.
in Step 3a above, λ l,m captures the correlations between label l and cluster m. If document d has label l, i.e., f d,l = 1, λ l,m contributes to π d,m , which is the prior of ψ d,m .