Pseudo-marginal bayesian multiple-class multiple-kernel learning for neuroimaging data

In clinical neuroimaging applications where subjects belong to one of multiple classes of disease states and multiple imaging sources are available, the aim is to achieve accurate classification while assessing the importance of the sources in the classification task. This work proposes the use of fully Bayesian multiple-class multiple-kernel learning based on Gaussian Processes, as it offers flexible classification capabilities and a sound quantification of uncertainty in parameter estimates and predictions. The exact inference of parameters and accurate quantification of uncertainty in Gaussian Process models, however, poses a computationally challenging problem. This paper proposes the application of advanced inference techniques based on Markov chain Monte Carlo and unbiased estimates of the marginal likelihood, and demonstrates their ability to accurately and efficiently carry out inference in their application on synthetic data and real clinical neuroimaging data. The results in this paper are important as they further work in the direction of achieving computationally feasible fully Bayesian models for a wide range of real world applications.