Journal
MEDICAL IMAGE ANALYSIS
Volume 24, Issue 1, Pages 190-204Publisher
ELSEVIER
DOI: 10.1016/j.media.2015.06.008
Keywords
SVM; Permutation tests; Analytic approximation
Categories
Funding
- NIH [R01AG01497113, P30 AG010129, K01 AG030514]
- National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering [U01 AG024904]
- Abbott
- AstraZeneca AB
- Bayer Schering Pharma AG
- Bristol-Myers Squibb
- Eisai Global Clinical Development
- Elan Corporation
- Genentech
- GE Healthcare
- GlaxoSmithKline
- Innogenetics
- Johnson and Johnson
- Eli Lilly and Co.
- Medpace, Inc.
- Merck and Co., Inc.
- Novartis AG
- Pfizer Inc.
- F. Hoffman-La Roche
- Schering-Plough
- Synarc, Inc.
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Dana Foundation
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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. (C) 2015 Elsevier B.V. All rights reserved.
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