Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data

• Main Author: Perez, Daniel Antonio
• Published: Georgia Institute of Technology 2010
• Subjects: Pattern recognition; Support vector machines; Relevance vector machines; Machine learning; FMRI; Diagnostic imaging; Magnetic resonance imaging; Imaging systems in medicine; Supervised learning (Machine learning)
• Online Access: http://hdl.handle.net/1853/34858

Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.