Title | SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data. |
Publication Type | Journal Article |
Year of Publication | 2018 |
Authors | Liu, Leo Yu- Feng, Yufeng Liu, and Hongtu Zhu |
Corporate Authors | Alzheimer's Disease Neuroimaging Initiative |
Journal | Neuroimage |
Volume | 175 |
Pagination | 230-245 |
Date Published | 2018 Jul 15 |
ISSN | 1095-9572 |
Keywords | Aged, Aged, 80 and over, Algorithms, Alzheimer Disease, Biomarkers, Brain, Classification, Cognitive Dysfunction, Female, Humans, Image Processing, Computer-Assisted, Male, Neuroimaging |
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. |
DOI | 10.1016/j.neuroimage.2018.03.040 |
Alternate Journal | Neuroimage |
Original Publication | SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data. |
PubMed ID | 29596980 |
PubMed Central ID | PMC6317520 |
Grant List | R01 GM126550 / GM / NIGMS NIH HHS / United States U01 AG024904 / AG / NIA NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R01 MH092335 / MH / NIMH NIH HHS / United States |