  Many datasets represent a combination of different ways of looking at the
same data that lead to different generalizations. For example, a corpus with
examples generated by different people may be mixtures of many perspectives and
can be viewed with different perspectives by others. It isnt always possible to
represent the viewpoints by a clean separation, in advance, of examples
representing each viewpoint and train a separate model for each viewpoint. We
introduce lensing, a mixed initiative technique to extract lenses or mappings
between machine learned representations and perspectives of human experts, and
to generate lensed models that afford multiple perspectives of the same
dataset. We apply lensing for two classes of latent variable models: a mixed
membership model, a matrix factorization model in the context of two mental
health applications, and we capture and imbue the perspectives of clinical
psychologists into these models. Our work shows the benefits of the machine
learning practitioner formally incorporating the perspective of a knowledgeable
domain expert into their models rather than estimating unlensed models
themselves in isolation.
