Scatter Balance: An Angle-Based Supervised Dimensionality Reduction

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Scatter Balance: An Angle-Based Supervised Dimensionality Reduction
	Liu, Shenglan 1; Feng, Lin 1; Qiao, Hong2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-02-01
卷号	26 期号:2 页码:277-289
关键词	Linear discriminant scatter matrix small sample size problem subspace selection
英文摘要	Subspace 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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	SAMPLE-SIZE PROBLEM ; ROBUST FEATURE-EXTRACTION ; MAXIMUM MARGIN CRITERION ; DISCRIMINANT-ANALYSIS ; FACE RECOGNITION ; DIRECT LDA ; CLASSIFICATION ; EFFICIENT ; ALGORITHM ; SELECTION
收录类别	SCI
语种	英语
WOS记录号	WOS:000348856200007
公开日期	2015-09-22
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/8059]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位	1.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
推荐引用方式 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.