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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
Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering
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论文类型：会议论文
发表时间：2016-01-01
收录刊物：EI、CPCI-S
卷号：9949
页面范围：87-95
关键字：EEG; Feature extraction; HALS; Tucker decomposition
摘要：Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It is illustrated that the proposed algorithm extracts physically meaningful features with relatively low storage and computational costs compared to the standard NTD model.