基于正则化Softmax回归的全脑功能性磁共振成像数据特征选择框架

发布时间：2018-04-08 12:45

  本文选题：功能性磁共振成像(fMRI)　切入点：过拟合　出处：《模式识别与人工智能》2016年07期

【摘要】：针对功能性磁共振成像(f MRI)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f MRI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f MRI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.
[Abstract]:In order to solve the problem of over-fitting of the classification model caused by the high dimensional and small sample characteristics of functional magnetic resonance imaging (fMRI) data, a framework for feature selection of global brain f MRI data combining L2 canonical and L1 regularization is proposed based on Softmax regression.First, the whole brain is divided into regions of interest and non-regions of interest based on the cognitive characteristics of the brain.Then, the region of interest is modeled by L _ 2 canonical which can reduce the weight coefficient to select all voxels of the region of interest, and the active voxels in the region of interest are obtained by using L _ 1 canonical model with sparse function.Finally, the regularized Softmax regression model of the global brain f MRI data is constructed by combining the voxels of the region of interest and the region of interest.Experiments on Haxby datasets show that the regularization strategies of L2 and L1 can effectively improve the accuracy of global classification.
【作者单位】： 北京工业大学计算机学院多媒体与智能软件技术北京市重点实验室;首都医科大学宣武医院;
【基金】：国家重点基础研究发展计划(973计划)项目(No.2014CB744601) 国家自然科学基金项目(No.61375059;61332016) 北大方正集团有限公司数字出版技术国家重点实验室开放课题资助~~
【分类号】：R445.2;O482.532
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本文编号：1721693