Scatter Balance: An Angle-Based Supervised Dimensionality Reduction
Liu, Shenglan1; Feng, Lin1; Qiao, Hong2
2015-02-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号26期号:2页码:277-289
文章类型Article
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.
关键词Linear Discriminant Scatter Matrix Small Sample Size Problem Subspace Selection
WOS标题词Science & Technology ; Technology
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研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000348856200007
引用统计
被引频次:11[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8059
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
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
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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.
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