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Rank-κ 2-D multinomial logistic regression for matrix data classification
Song, Kun1; Nie, Feiping2; Han, Junwei1; Li, Xuelong3
Department光学影像学习与分析中心
2018-08
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162237X;21622388
Volume29Issue:8Pages:3524-3537
Contribution Rank3
AbstractThe 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.
DOI10.1109/TNNLS.2017.2731999
Indexed ByEI
Language英语
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20173504106175
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30846
Collection光学影像学习与分析中心
Corresponding AuthorHan, Junwei
Affiliation1.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
Recommended Citation
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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