中文摘要：

针对功能性磁共振成像（fMRI）数据高维小样本特性给分类模型带来的过拟合问题，文中基于Softmax回归提出结合L2正则与L1正则的全脑fMRI数据特征选择框架．首先，基于大脑认知的特点，将全脑分成感兴趣区域和非感兴趣区域．然后，使用可以缩小权值系数的L2正则对感兴趣区域建模以选出感兴趣区域的全部体素，使用具有稀疏作用的L1正则对非感兴趣区域建模以选出非感兴趣区域中的激活体素．最后，结合感兴趣区域和非感兴趣区域的体素构成全脑fMRI数据的正则化Softmax回归模型．在Haxby数据集上的实验表明，L2与L1的正则化策略可有效提升全脑分类的准确率．

英文摘要：

To solve the classification model overfitting problem caused by the high dimension and small sample properties of functional magnetic resonance imaging （fMRI） data, a feature selection framework of whole-brain fMRI data combining L1-norm regularization and L2-norm regularization in softmax regression is proposed. Firstly, the whole brain is divided into the region of interest （ ROI ） and the region of non-interest （RONI） in terms of the characteristics of brain cognition. Then, L2-norm regularization shrinking the weighting coefficients is used to model all voxels in ROI while L1-norm regularization with a sparse effect is employed for modeling the activated voxels in RONI. Finally, the regularized softmax regression model of whole-brain fMRI data is constructed by integrating all voxels in ROI and the activated voxels in RONI. The experimental results on Haxby datasets show that the regularization strategies of L2-norm and L1-norm effectively improve the whole-brain classification performance compared to some other methods.