title: Interpreting support vector machine models for multivariate group wise analysis in neuroimaging.
abstract: Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier's decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification.
authors.affiliation: Center for Biomedical Image Computing and Analytics, United States. Electronic address: bilwaj@gmail.com.
authors.forename: Bilwaj
authors.initials: B
authors.lastname: Gaonkar
authors.affiliation: Center for Biomedical Image Computing and Analytics, United States; Department of Biostatistics and Epidemiology Perelman School of Medicine, University of Pennsylvania, United States.
authors.forename: Russell
authors.initials: R
authors.lastname: T Shinohara
authors.affiliation: Center for Biomedical Image Computing and Analytics, United States.
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Analytic approximation; Permutation tests; SVM