Rank-κ 2-D multinomial logistic regression for matrix data classification
文献类型：期刊论文
| 作者 | Song, Kun1; Nie, Feiping2; Han, Junwei1; Li, Xuelong3 |
| 刊名 | IEEE Transactions on Neural Networks and Learning Systems
 |
| 出版日期 | 2018-08 |
| 卷号 | 29期号:8页码:3524-3537 |
| ISSN号 | 2162237X;21622388 |
| DOI | 10.1109/TNNLS.2017.2731999 |
| 产权排序 | 3 |
| 英文摘要 | 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. |
| 语种 | 英语 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| 源URL | [http://ir.opt.ac.cn/handle/181661/30846]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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