CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction
Nie, Feiping1; Xiang, Shiming2; Liu, Yun3; Hou, Chenping1; Zhang, Changshui1
Source PublicationPATTERN RECOGNITION LETTERS
2012-04-01
Volume33Issue:5Pages:485-491
SubtypeArticle
AbstractIn this paper, a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative power between classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squares discriminant analysis methods are developed under the orthogonal or the uncorrelated constraint. We show that the orthogonal least squares discriminant analysis is an extension to the null space linear discriminant analysis, and the uncorrelated least squares discriminant analysis is exactly equivalent to the traditional linear discriminant analysis. Comparative experiments show that the orthogonal one is more preferable for real world applications. (C) 2011 Elsevier B.V. All rights reserved.
KeywordLeast Squares Subspace Learning Discriminant Analysis Feature Extraction Image Recognition
WOS HeadingsScience & Technology ; Technology
WOS KeywordFACE RECOGNITION ; PRESERVING PROJECTIONS ; REGRESSION ; REDUCTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000301212400001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3723
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Tsinghua Univ, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China
2.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
3.Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
Recommended Citation
GB/T 7714
Nie, Feiping,Xiang, Shiming,Liu, Yun,et al. Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction[J]. PATTERN RECOGNITION LETTERS,2012,33(5):485-491.
APA Nie, Feiping,Xiang, Shiming,Liu, Yun,Hou, Chenping,&Zhang, Changshui.(2012).Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction.PATTERN RECOGNITION LETTERS,33(5),485-491.
MLA Nie, Feiping,et al."Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction".PATTERN RECOGNITION LETTERS 33.5(2012):485-491.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Nie, Feiping]'s Articles
[Xiang, Shiming]'s Articles
[Liu, Yun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Nie, Feiping]'s Articles
[Xiang, Shiming]'s Articles
[Liu, Yun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Nie, Feiping]'s Articles
[Xiang, Shiming]'s Articles
[Liu, Yun]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.