SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data.

TitleSMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data.
Publication TypeJournal Article
Year of Publication2018
AuthorsLiu, Leo Yu- Feng, Yufeng Liu, and Hongtu Zhu
Corporate AuthorsAlzheimer's Disease Neuroimaging Initiative
JournalNeuroimage
Volume175
Pagination230-245
Date Published2018 07 15
ISSN1095-9572
KeywordsAged, 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.

DOI10.1016/j.neuroimage.2018.03.040
Alternate JournalNeuroimage
Original PublicationSMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data.
PubMed ID29596980
PubMed Central IDPMC6317520
Grant ListR01 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