Scatter Balance: An Angle-Based Supervised Dimensionality Reduction
Liu, Shenglan1; Feng, Lin1; Qiao, Hong2
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2015-02-01
Volume26Issue:2Pages:277-289
SubtypeArticle
AbstractSubspace selection is widely applied in data classification, clustering, and visualization. The samples projected into subspace can be processed efficiently. In this paper, we research the linear discriminant analysis (LDA) and maximum margin criterion (MMC) algorithms intensively and analyze the effects of scatters to subspace selection. Meanwhile, we point out the boundaries of scatters in LDA and MMC algorithms to illustrate the differences and similarities of subspace selection in different circumstances. Besides, the effects of outlier classes on subspace selection are also analyzed. According to the above analysis, we propose a new subspace selection method called angle linear discriminant embedding (ALDE) on the basis of angle measurement. ALDE utilizes the cosine of the angle to get new within-class and between-class scatter matrices and avoids the small sample size problem simultaneously. To deal with high-dimensional data, we extend ALDE to a two-stage ALDE (TS-ALDE). The synthetic data experiments indicate that ALDE can balance the within-class and between-class scatters and be robust to outlier classes. The experimental results based on UCI machine-learning repository and image databases show that TS-ALDE has a lower time complexity than ALDE while processing high-dimensional data.
KeywordLinear Discriminant Scatter Matrix Small Sample Size Problem Subspace Selection
WOS HeadingsScience & Technology ; Technology
WOS KeywordSAMPLE-SIZE PROBLEM ; ROBUST FEATURE-EXTRACTION ; MAXIMUM MARGIN CRITERION ; DISCRIMINANT-ANALYSIS ; FACE RECOGNITION ; DIRECT LDA ; CLASSIFICATION ; EFFICIENT ; ALGORITHM ; SELECTION
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000348856200007
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8059
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Liu, Shenglan,Feng, Lin,Qiao, Hong. Scatter Balance: An Angle-Based Supervised Dimensionality Reduction[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(2):277-289.
APA Liu, Shenglan,Feng, Lin,&Qiao, Hong.(2015).Scatter Balance: An Angle-Based Supervised Dimensionality Reduction.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(2),277-289.
MLA Liu, Shenglan,et al."Scatter Balance: An Angle-Based Supervised Dimensionality Reduction".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.2(2015):277-289.
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
[Liu, Shenglan]'s Articles
[Feng, Lin]'s Articles
[Qiao, Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Shenglan]'s Articles
[Feng, Lin]'s Articles
[Qiao, Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Shenglan]'s Articles
[Feng, Lin]'s Articles
[Qiao, Hong]'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.