SMAC：基于空间多类别角度的高维神经影像数据分类器。
SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data.
机构信息
Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center (LCCC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
出版信息
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.
随着先进成像技术的发展，科学家们对鉴定与各种癌症、神经精神疾病和神经退行性疾病等不同亚型或过渡阶段相关的成像生物标志物很感兴趣。在本文中，我们提出了一种新颖的基于空间多类别角度的分类器（SMAC），用于高效识别这种成像生物标志物。所提出的 SMAC 不仅利用了高维成像数据的空间结构，而且还处理了二进制和多类别分类问题。我们引入了一种基于增广拉格朗日乘子法的有效算法来解决 SMAC 的大规模优化问题。我们的模拟和真实数据实验都证明了 SMAC 的有用性。
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