Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data
Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data
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基于平滑 L0 范数的改进稀疏分解，使用拉普拉斯核从 fMRI 数据中选择特征
DOI：
10.1016/j.jneumeth.2014.12.021
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发表时间：
2015-04-30
影响因子：
3
通讯作者：
Long, Zhiying
中科院分区：
文献类型：
--
作者：
Zhang, Chuncheng;Song, Sutao;Long, Zhiying
背景资料：功能磁共振成像（fMRI）数据具有“少样本、大特征”的特点，在基于fMRI的解码过程中，特征选择对于提高多变量分类技术的分类精度具有重要作用。最近，一些稀疏表示方法已被应用于fMRI数据的体素选择。尽管稀疏表示方法的计算效率低，他们仍然表现出的应用程序，选择功能从fMRI data.New方法的承诺：在这项研究中，我们提出了Laplacian平滑L0范数（LSL 0）的功能磁共振成像数据的特征选择方法。基于平滑L0范数（SL0）的快速稀疏分解结果：仿真和真实的fMRI数据的实验结果证明了LSL 0方法在稀疏源估计和特征选择方面的可行性和鲁棒性。仿真结果表明，在高噪声水平下，LSL 0比SL 0产生更准确的源估计。在模拟和真实的fMRI实验中，使用LSL 0选择的体素的分类准确率均高于SL 0。此外，LSL 0和SL 0都表现出更高的分类准确率，并且比伊卡和fMRI解码t检验所需的时间更少。结论：LSL 0在高噪声水平下的稀疏源估计和特征选择方面优于SL 0。此外，LSL 0和SL 0表现出更好的性能比伊卡和t检验的特征选择。(C)2015 Elsevier B.V.版权所有。
Background: Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data.New method: In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources.Results: Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection.Comparison with existing methods: Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding.Conclusions: LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection. (C) 2015 Elsevier B.V. All rights reserved.