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| Feature Selection Framework of Whole-Brain Functional Magnetic Resonance Imaging Data Based on Regularized Softmax Regression |
| QU Yongkang1, JI Junzhong1, LIANG Peipeng2, GAO Mingxia1 |
1.Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science, Beijing University of Technology, Beijing 100124
2.Xuanwu Hospital, Capital Medical University, Beijing 100053 |
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Abstract 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.
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Received: 22 October 2015
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| About author:: QU Yongkang, born in 1989, master student. His research interests include machine learning and biological data miningJI Junzhong(Corresponding author), born in 1969, Ph.D., professor. His research interests include machine learning, data mining, swarm intelligence and bioinformatics.LIANG Peipeng, born in 1979, Ph.D., associate professor. His research interests include medical imaging and cognitive neuroscience.GAO Mingxia, born in 1973, Ph.D.. Her research interests include data mining, semantic web and knowledge engineering. |
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2016, Vol. 29 
