A new model is developed for joint analysis of ordered, categorical, real and count data. The ordered and categorical data are answers to questionnaires, the (word) count data correspond to the text questions from the questionnaires, and the real data correspond to fMRI responses for each subject. The Bayesian model employs the von Mises distribution in a novel manner to infer sparse graphical models jointly across people, questions, fMRI stimuli and brain region, with this integrated within a new matrix factorization based on latent binary features. The model is compared with simpler alternatives on two real datasets. We also demonstrate the ability to predict the response of the brain to visual stimuli (as measured by fMRI), based on knowledge of how the associated person answered classical questionnaires. Copyright 2013 by the author(s).
|Original language||English (US)|
|Title of host publication||30th International Conference on Machine Learning, ICML 2013|
|Publisher||International Machine Learning Society (IMLS)firstname.lastname@example.org|
|Number of pages||9|
|State||Published - Jan 1 2013|