کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8686877 | 1580835 | 2018 | 50 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 175, 15 July 2018, Pages 230-245
Journal: NeuroImage - Volume 175, 15 July 2018, Pages 230-245
نویسندگان
Leo Yu-Feng Liu, Yufeng Liu, Hongtu Zhu, for the Alzheimer's Disease Neuroimaging Initiative for the Alzheimer's Disease Neuroimaging Initiative,