  A main goal of data visualization is to find, from among all the available
alternatives, mappings to the 2D/3D display which are relevant to the user.
Assuming user interaction data, or other auxiliary data about the items or
their relationships, the goal is to identify which aspects in the primary data
support the user\'s input and, equally importantly, which aspects of the
user\'s potentially noisy input have support in the primary data. For solving
the problem, we introduce a multi-view embedding in which a latent
factorization identifies which aspects in the two data views (primary data and
user data) are related and which are specific to only one of them. The
factorization is a generative model in which the display is parameterized as a
part of the factorization and the other factors explain away the aspects not
expressible in a two-dimensional display. Functioning of the model is
demonstrated on several data sets.

Machine Learning;Information Retrieval