标题：
Local discriminant hyperalignment for multi-subject fMRI data alignment
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所属单位：
计算机科学与技术学院/人工智能学院/软件学院
发表刊物：
AAAI Conf. Artif. Intell., AAAI
摘要：
Multivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. Hyperalignment (HA) is one of the most effective functional alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel supervised HA method, which can provide better functional alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms. Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
是否译文：
否
发表时间：
2017-01-01
合写作者：
张道强
通讯作者：
YOUSEFNEZHAD MUHAMMAD
发表时间：
2017-01-01
学位：工学博士学位
所在单位：计算机科学与技术学院/人工智能学院/软件学院