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个人信息Personal Information
教授
博士生导师
硕士生导师
主要任职：人力资源处处长（党委教师工作部部长、党委人才办公室主任）【兼党委组织部副部长】
性别：男
毕业院校：上海交通大学
学位：博士
所在单位：人力资源处（党委教师工作部、党委人才办公室）
学科：生物医学工程. 信号与信息处理. 模式识别与智能系统
电子邮箱：cong@dlut.edu.cn
LOW-RANK APPROXIMATION BASED NON-NEGATIVE MULTI-WAY ARRAY DECOMPOSITION ON EVENT-RELATED POTENTIALS
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论文类型：期刊论文
发表时间：2014-12-01
发表刊物：INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
收录刊物：SCIE、EI、PubMed
卷号：24
期号：8
页面范围：CP2-U20
ISSN号：0129-0657
关键字：Event-related potential; low-rank approximation; multi-domain feature; non-negative canonical polyadic decomposition; non-negative tensor factorization; tensor decomposition
摘要：Non-negative tensor factorization (NTF) has been successfully applied to analyze event-related potentials (ERPs), and shown superiority in terms of capturing multi-domain features. However, the time-frequency representation of ERPs by higher-order tensors are usually large-scale, which prevents the popularity of most tensor factorization algorithms. To overcome this issue, we introduce a non-negative canonical polyadic decomposition (NCPD) based on low-rank approximation (LRA) and hierarchical alternating least square (HALS) techniques. We applied NCPD (LRAHALS and benchmark HALS) and CPD to extract multi-domain features of a visual ERP. The features and components extracted by LRAHALS NCPD and HALS NCPD were very similar, but LRAHALS NCPD was 70 times faster than HALS NCPD. Moreover, the desired multi-domain feature of the ERP by NCPD showed a significant group difference (control versus depressed participants) and a difference in emotion processing (fearful versus happy faces). This was more satisfactory than that by CPD, which revealed only a group difference.