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Dimensionality Reduction in Multiple Ordinal Regression

文献类型：期刊论文

作者Zeng, Jiabei5; Liu, Yang1,4; Leng, Biao3; Xiong, Zhang3; Cheung, Yiu-ming1,2,4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2018-09-01
卷号29期号:9页码:4088-4101
关键词Dimensionality reduction (DR) multiple labels ordinal regression supervised
ISSN号2162-237X
DOI10.1109/TNNLS.2017.2752003
英文摘要Supervised dimensionality reduction (DR) plays an important role in learning systems with high-dimensional data. It projects the data into a low-dimensional subspace and keeps the projected data distinguishable in different classes. In addition to preserving the discriminant information for binary or multiple classes, some real-world applications also require keeping the preference degrees of assigning the data to multiple aspects, e.g., to keep the different intensities for co-occurring facial expressions or the product ratings in different aspects. To address this issue, we propose a novel supervised DR method for DR in multiple ordinal regression (DRMOR), whose projected subspace preserves all the ordinal information in multiple aspects or labels. We formulate this problem as a joint optimization framework to simultaneously perform DR and ordinal regression. In contrast to most existing DR methods, which are conducted independently of the subsequent classification or ordinal regression, the proposed framework fully benefits from both of the procedures. We experimentally demonstrate that the proposed DRMOR method (DRMOR-M) well preserves the ordinal information from all the aspects or labels in the learned subspace. Moreover, DRMOR-M exhibits advantages compared with representative DR or ordinal regression algorithms on three standard data sets.
资助项目National Natural Science Foundation of China[61472023] ; National Natural Science Foundation of China[61503317] ; National Natural Science Foundation of China[61272366] ; National Natural Science Foundation of China[61672444] ; National Natural Science Foundation of China[61702481] ; SZSTI[JCYJ20160531194006833] ; Faculty Research Grant of Hong Kong Baptist University[FRG2/16-17/032] ; Faculty Research Grant of Hong Kong Baptist University[FRG2/15-16/049] ; Faculty Research Grant of Hong Kong Baptist University[FRG2/16-17/051]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000443083700013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/4997]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Leng, Biao
作者单位1.Hong Kong Baptist Univ, Inst Res & Continuing Educ, Shenzhen 518057, Peoples R China
2.Beijing Normal Univ, Hong Kong Baptist Univ, United Int Coll, Zhuhai 519087, Peoples R China
3.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China
4.Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zeng, Jiabei,Liu, Yang,Leng, Biao,et al. Dimensionality Reduction in Multiple Ordinal Regression[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(9):4088-4101.
APA Zeng, Jiabei,Liu, Yang,Leng, Biao,Xiong, Zhang,&Cheung, Yiu-ming.(2018).Dimensionality Reduction in Multiple Ordinal Regression.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(9),4088-4101.
MLA Zeng, Jiabei,et al."Dimensionality Reduction in Multiple Ordinal Regression".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.9(2018):4088-4101.

入库方式： OAI收割

来源：计算技术研究所

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