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

Neuroimage. 2018 Jul 15;175:230-245. doi: 10.1016/j.neuroimage.2018.03.040. Epub 2018 Mar 27.


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: ADMM; Alzheimer's disease; Angle-based classifier; Fused lasso; Large margin classifier; Neuroimaging classification.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Alzheimer Disease / diagnostic imaging*
  • Biomarkers
  • Brain / diagnostic imaging*
  • Classification
  • Cognitive Dysfunction / diagnostic imaging*
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Male
  • Neuroimaging / methods*


  • Biomarkers