基于多任务特征学习的功能连接图联合特征提取

A. Altmann, B. Ng
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引用次数: 1

摘要

在分类器学习中使用稀疏正则化是一种很有吸引力的策略,可以在高维脑成像数据中定位相关脑区域和区域之间的连接。稀疏分类器学习的一个主要缺点是对数据扰动缺乏稳定性,这导致选择不同的特征集。在这里,我们提出使用多任务特征学习(MFL)来生成稀疏和稳定的分类器。在静息状态功能磁共振成像(fMRI)估计的功能连接的分类实验中,我们表明,与标准稀疏分类器相比,MFL在多类设置下更一致地选择了相同的连接,并提供了更多的可解释模型,同时实现了相似的分类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Feature Extraction from Functional Connectivity Graphs with Multi-task Feature Learning
Using sparse regularization in classifier learning is an appealing strategy to locate relevant brain regions and connections between regions within high-dimensional brain imaging data. A major drawback of sparse classifier learning is the lack of stability to data perturbations, which leads to different sets of features being selected. Here, we propose to use multi-task feature learning (MFL) to generate sparse and stable classifiers. In classification experiments on functional connectivity estimated from resting state functional magnetic resonance imaging (fMRI), we show that MFL more consistently selects the same connections across bootstrap samples and provides more interpretable models in multiclass settings than standard sparse classifiers, while achieving similar classification performance.
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