文献详情
Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification
文献类型期刊
作者Feng, Xiaodong[1];Jiao, Yuting[2];Lv, Chuan[3];Zhou, Dong[4]
机构
通讯作者Jiao, YT (reprint author), Sci & Technol Reliabil & Environm Engn Lab, Beijing 100191, Peoples R China.
来源信息年:2016  卷:52  页码范围:161-167  
期刊信息ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE影响因子和分区  ISSN:0952-1976
关键词Non-negative matrix factorization; Semi-supervised learning; Label consistent regularization; Maintenance activities identification; PHM data challenge
增刊正刊
摘要Health prognostic is playing an increasingly essential role in product and system management, for which non-negative matrix factorization (NMF) has been an effective method to model the high dimensional recorded data of the device or system. However, the existing unsupervised and supervised NMF models fail to learn from both labeled and unlabeled data together. Therefore, we propose a label consistent semi-supervised non-negative matrix factorization (LCSSNMF) framework that can simultaneously factorize both labeled and unlabeled data, where the discriminability of label data is preserved. Specifically, it firstly incorporates a class-wise coefficient distance regularization term that makes the coefficients for similar samples or samples with the same label close. Moreover, a label reconstruction regularization term is also presented, as the classification error with coefficient matrix of labeled data is expected as low as possible, which will potentially improve the classification accuracy in maintenance activities identification for industrial remote monitoring and diagnostics. The experiment results on real maintenance activities identification application from PHM 2013 data challenge competition demonstrate that LCSSNMF outperforms the state-of-arts NMF methods and results provided by the competition. (C) 2016 Elsevier Ltd. All rights reserved.
收录情况SCIE(WOS:000379631100015)  EI(20161302174038)  
所属部门计算机学院;可靠性与系统工程学院
DOI10.1016/j.engappai.2016.02.016
百度学术Label consistent semi-supervised non-negative matrix factorization for maintenance activities identification
语言外文
被引频次77
人气指数86
浏览次数86
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