Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8686877 | NeuroImage | 2018 | 50 Pages |
Abstract
With the development of advanced imaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases, among many others. In this paper, we propose a novel spatial multi-category angle-based classifier (SMAC) for the efficient identification of such imaging biomarkers. The proposed SMAC not only utilizes the spatial structure of high-dimensional imaging data but also handles both binary and multi-category classification problems. We introduce an efficient algorithm based on an alternative direction method of multipliers to solve the large-scale optimization problem for SMAC. Both our simulation and real data experiments demonstrate the usefulness of SMAC.
Keywords
Related Topics
Life Sciences
Neuroscience
Cognitive Neuroscience
Authors
Leo Yu-Feng Liu, Yufeng Liu, Hongtu Zhu, for the Alzheimer's Disease Neuroimaging Initiative for the Alzheimer's Disease Neuroimaging Initiative,