Algorithm 1:

Generative process for the sLDA model.

For each individual d ∈ [1,...,D]:

a. Draw topic mixture proportions:   θd ~ DirichletK(α),   where α is a hyper-parameter.

b. For each “word” n ∈ [1,...,Nd] within individual d:   i. Draw a topic assignment: zd,n ~ Multinomial(θd).   ii. Draw an image / genetic feature: wd,n ~ Multinomial(βzd,n).

c. Draw the discrete response variable:   $y_d\mid z_{d,1:N_d},\mu ~\mathrm{softmax}\left({\mu }^T{\overline{z}}_d\right)$   where ${\overline{z}}_d=\frac{1}{N_d}{\sum }_{n=1}^{N_d}{z}_{d,n}$ is the empirical frequencyvector of different topics in subject d, and μ is the classification weighting matrix to be estimated.