Dimensionality Reduction in Multiple Ordinal Regression

doi:10.1109/TNNLS.2017.2752003

	Dimensionality Reduction in Multiple Ordinal Regression
	Zeng, Jiabei 1; Liu, Yang 2,3; Leng, Biao 4; Xiong, Zhang 4; Cheung, Yiu-ming 2,3,5
	2018-09-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	29 期号:9 页码:4088-4101
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.
关键词	Dimensionality reduction (DR) multiple labels ordinal regression supervised
DOI	10.1109/TNNLS.2017.2752003
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000443083700013
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/4998
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.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China 3.Hong Kong Baptist Univ, Inst Res & Continuing Educ, Shenzhen 518057, Peoples R China 4.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China 5.Beijing Normal Univ, Hong Kong Baptist Univ, United Int Coll, Zhuhai 519087, 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.