Multivariate Multilinear Regression
文献类型：期刊论文
| 作者 | Su, Ya1; Gao, Xinbo2; Li, Xuelong3; Tao, Dacheng4 |
| 刊名 | ieee transactions on systems man and cybernetics part b-cybernetics
 |
| 出版日期 | 2012-12-01 |
| 卷号 | 42期号:6页码:1560-1573 |
| 关键词 | Active appearance model (AAM) multivariate linear regression (MLR) principal component regression (PCR) under sample problem (USP) |
| ISSN号 | 10834419 |
| 产权排序 | 3 |
| 合作状况 | 国际 |
| 中文摘要 | conventional regression methods, such as multivariate linear regression (mlr) and its extension principal component regression (pcr), deal well with the situations that the data are of the form of low-dimensional vector. when the dimension grows higher, it leads to the under sample problem (usp): the dimensionality of the feature space is much higher than the number of training samples. however, little attention has been paid to such a problem. this paper first adopts an in-depth investigation to the usp in pcr, which answers three questions: 1) why is usp produced? 2) what is the condition for usp, and 3) how is the influence of usp on regression. with the help of the above analysis, the principal components selection problem of pcr is presented. subsequently, to address the problem of pcr, a multivariate multilinear regression (mmr) model is proposed which gives a substitutive solution to mlr, under the condition of multilinear objects. the basic idea of mmr is to transfer the multilinear structure of objects into the regression coefficients as a constraint. as a result, the regression problem is reduced to find two low-dimensional coefficients so that the principal components selection problem is avoided. moreover, the sample size needed for solving mmr is greatly reduced so that usp is alleviated. as there is no closed-form solution for mmr, an alternative projection procedure is designed to obtain the regression matrices. for the sake of completeness, the analysis of computational cost and the proof of convergence are studied subsequently. furthermore, mmr is applied to model the fitting procedure in the active appearance model (aam). experiments are conducted on both the carefully designed synthesizing data set and aam fitting databases verified the theoretical analysis. |
| 英文摘要 | conventional regression methods, such as multivariate linear regression (mlr) and its extension principal component regression (pcr), deal well with the situations that the data are of the form of low-dimensional vector. when the dimension grows higher, it leads to the under sample problem (usp): the dimensionality of the feature space is much higher than the number of training samples. however, little attention has been paid to such a problem. this paper first adopts an in-depth investigation to the usp in pcr, which answers three questions: 1) why is usp produced? 2) what is the condition for usp, and 3) how is the influence of usp on regression. with the help of the above analysis, the principal components selection problem of pcr is presented. subsequently, to address the problem of pcr, a multivariate multilinear regression (mmr) model is proposed which gives a substitutive solution to mlr, under the condition of multilinear objects. the basic idea of mmr is to transfer the multilinear structure of objects into the regression coefficients as a constraint. as a result, the regression problem is reduced to find two low-dimensional coefficients so that the principal components selection problem is avoided. moreover, the sample size needed for solving mmr is greatly reduced so that usp is alleviated. as there is no closed-form solution for mmr, an alternative projection procedure is designed to obtain the regression matrices. for the sake of completeness, the analysis of computational cost and the proof of convergence are studied subsequently. furthermore, mmr is applied to model the fitting procedure in the active appearance model (aam). experiments are conducted on both the carefully designed synthesizing data set and aam fitting databases verified the theoretical analysis. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | automation & control systems ; computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | automation & control systems ; computer science |
| 关键词[WOS] | principal component regression ; discriminant-analysis ; gait recognition ; tensor analysis ; appearance ; representation ; tracking ; models ; selection ; objects |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000311353700005 |
| 公开日期 | 2013-07-01 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/20842]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China 2.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China 4.Univ Technol, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia |
| 推荐引用方式 GB/T 7714 | Su, Ya,Gao, Xinbo,Li, Xuelong,et al. Multivariate Multilinear Regression[J]. ieee transactions on systems man and cybernetics part b-cybernetics,2012,42(6):1560-1573. |
| APA | Su, Ya,Gao, Xinbo,Li, Xuelong,&Tao, Dacheng.(2012).Multivariate Multilinear Regression.ieee transactions on systems man and cybernetics part b-cybernetics,42(6),1560-1573. |
| MLA | Su, Ya,et al."Multivariate Multilinear Regression".ieee transactions on systems man and cybernetics part b-cybernetics 42.6(2012):1560-1573. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
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。