Rank-κ 2-D multinomial logistic regression for matrix data classification

doi:10.1109/TNNLS.2017.2731999

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	Rank-κ 2-D multinomial logistic regression for matrix data classification
	Song, Kun 1; Nie, Feiping 2; Han, Junwei 1; Li, Xuelong 3
作者部门	光学影像学习与分析中心
	2018-08
发表期刊	IEEE Transactions on Neural Networks and Learning Systems
ISSN	2162237X;21622388
卷号	29 期号:8 页码:3524-3537
产权排序	3
摘要	The amount of matrix data has increased rapidly nowadays. How to classify matrix data efficiently is an important issue. In this paper, by discovering the shortages of 2-D linear discriminant analysis and 2-D logistic regression, a novel 2-D framework named rank- κ 2-D multinomial logistic regression (2DMLR-RK) is proposed. The 2DMLR-RK is designed for a multiclass matrix classification problem. In the proposed framework, each category is modeled by a left projection matrix and a right projection matrix with rank κ. The left projection matrices capture the row information of matrix data, and the right projection matrices acquire the column information. We identify the parameter κ plays the role of balancing the capacity of learning and generalization of the 2DMLR-RK. In addition, we develop an effective framework for solving the proposed nonconvex optimization problem. The convergence, initialization, and computational complexity are discussed. Extensive experiments on various types of data sets are conducted. Comparing with 1-D methods, 2DMLR-RK not only achieves a better classification accuracy, but also costs less computation time. Comparing with other state-of-the-art 2-D methods, the 2DMLR-RK achieves a better performance for matrix data classification. © 2012 IEEE.
DOI	10.1109/TNNLS.2017.2731999
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20173504106175
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/30846
专题	光谱成像技术研究室
通讯作者	Han, Junwei
作者单位	1.School of Automation, Northwestern Polytechnical University, Xi'an; 710072, China; 2.Center for Optical Imagery Analysis and Learning, School of Computer Science, Northwestern Polytechnical University, Xi'an; 710072, China; 3.Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China
推荐引用方式 GB/T 7714	Song, Kun,Nie, Feiping,Han, Junwei,et al. Rank-κ 2-D multinomial logistic regression for matrix data classification[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(8):3524-3537.
APA	Song, Kun,Nie, Feiping,Han, Junwei,&Li, Xuelong.(2018).Rank-κ 2-D multinomial logistic regression for matrix data classification.IEEE Transactions on Neural Networks and Learning Systems,29(8),3524-3537.
MLA	Song, Kun,et al."Rank-κ 2-D multinomial logistic regression for matrix data classification".IEEE Transactions on Neural Networks and Learning Systems 29.8(2018):3524-3537.

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