OPT OpenIR  > 光学影像学习与分析中心
Rank-κ 2-D multinomial logistic regression for matrix data classification
Song, Kun1; Nie, Feiping2; Han, Junwei1; Li, Xuelong3
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
ISSN2162237X;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.
DOI10.1109/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
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30846
专题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.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Rank-κ 2-D multinomi(2627KB)期刊论文出版稿开放获取CC BY-NC-SA浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Song, Kun]的文章
[Nie, Feiping]的文章
[Han, Junwei]的文章
百度学术
百度学术中相似的文章
[Song, Kun]的文章
[Nie, Feiping]的文章
[Han, Junwei]的文章
必应学术
必应学术中相似的文章
[Song, Kun]的文章
[Nie, Feiping]的文章
[Han, Junwei]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Rank-κ 2-D multinomial logistic regression for matrix data classification.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。