multi-level multi-task structured sparse learning for diagnosis of schizophrenia disease
发表时间：2020-01-13 点击次数：
所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：lect. notes comput. sci.
摘要：in recent studies, it has attracted increasing attention in multi-frequency bands analysis for diagnosis of schizophrenia (sz). however, most existing feature selection methods designed for multi-frequency bands analysis do not take into account the inherent structures (i.e., both frequency specificity and complementary information) from multi-frequency bands in the model, which are limited to identify the discriminative feature subset in a single step. to address this problem, we propose a multi-level multi-task structured sparse learning (mlmt-ts) framework to explicitly consider the common features with a hierarchical structure. specifically, we introduce two regularization terms in the hierarchical framework to impose the common features across different bands and the specificity from individuals. then, the selected features are used to construct multiple support vector machine (svm) classifiers. finally, we adopt an ensemble strategy to combine outputs of all svm classifiers to achieve the final decision. our method has been evaluated on 46 subjects, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods. © springer international publishing ag 2017.
issn号：0302-9743
是否译文：否
发表时间：2017-01-01
合写作者：wang, mingliang,hao, xiaoke,huang, jiashuang,wang, kangcheng,xu, xijia
通讯作者：张道强
发表时间：2017-01-01
发表刊物：lect. notes comput. sci.
摘要：in recent studies, it has attracted increasing attention in multi-frequency bands analysis for diagnosis of schizophrenia (sz). however, most existing feature selection methods designed for multi-frequency bands analysis do not take into account the inherent structures (i.e., both frequency specificity and complementary information) from multi-frequency bands in the model, which are limited to identify the discriminative feature subset in a single step. to address this problem, we propose a multi-level multi-task structured sparse learning (mlmt-ts) framework to explicitly consider the common features with a hierarchical structure. specifically, we introduce two regularization terms in the hierarchical framework to impose the common features across different bands and the specificity from individuals. then, the selected features are used to construct multiple support vector machine (svm) classifiers. finally, we adopt an ensemble strategy to combine outputs of all svm classifiers to achieve the final decision. our method has been evaluated on 46 subjects, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods. © springer international publishing ag 2017.
issn号：0302-9743
是否译文：否
发表时间：2017-01-01
合写作者：wang, mingliang,hao, xiaoke,huang, jiashuang,wang, kangcheng,xu, xijia
通讯作者：张道强
发表时间：2017-01-01
k8凯发集团的版权所有©2018- 南京航空航天大学·信息化处（信息化技术中心）
访问量： 本月访问： 今日访问量： 最后更新时间：--