SCALABLE FUSED LASSO SVM FOR CONNECTOME-BASED DISEASE PREDICTION.
SCALABLE FUSED LASSO SVM FOR CONNECTOME-BASED DISEASE PREDICTION.
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DOI：
10.1109/icassp.2014.6854753
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Sripada CS
中科院分区：
文献类型：
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作者：
Watanabe T;Scott CD;Kessler D;Angstadt M;Sripada CS
人们对开发基于机器的方法有很大的兴趣，这些方法使用从静息状态fMRI生成的被称为功能连接体（FC）的高维相关图来可靠地区分患者和健康对照。为了解决FC的维数，目前的工作机构依赖于特征选择技术，是盲目的数据的空间结构。在本文中，我们建议使用融合Lasso正则化支持向量机来明确地解释FC的6-D结构（由3-D大脑空间中的点对定义）。为了解决由此产生的非光滑和大规模的优化问题，我们介绍了一种新的和可扩展的算法的基础上交替方向法。对真实的静息状态扫描的实验表明，我们的方法可以恢复比以前的方法更神经科学信息的结果。
There is substantial interest in developing machine-based methods that reliably distinguish patients from healthy controls using high dimensional correlation maps known as functional connectomes (FC's) generated from resting state fMRI. To address the dimensionality of FC's, the current body of work relies on feature selection techniques that are blind to the spatial structure of the data. In this paper, we propose to use the fused Lasso regularized support vector machine to explicitly account for the 6-D structure of the FC (defined by pairs of points in 3-D brain space). In order to solve the resulting nonsmooth and large-scale optimization problem, we introduce a novel and scalable algorithm based on the alternating direction method. Experiments on real resting state scans show that our approach can recover results that are more neuroscientifically informative than previous methods.