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

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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.

Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479952083
Publication statusPublished - 4 Dec 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014


Conference22nd International Conference on Pattern Recognition, ICPR 2014


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