Learning Regularized LDA by Clustering | |
Pang, Yanwei1; Wang, Shuang1; Yuan, Yuan2 | |
作者部门 | 光学影像学习与分析中心 |
2014-12-01 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
卷号 | 25期号:12页码:2191-2201 |
产权排序 | 2 |
摘要 | As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between-and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones. To overcome the problem without increasing the number of training samples, we propose making use of the structure of the given training data to regularize the between- and within-class scatter matrices by between-and within-cluster scatter matrices, respectively, and simultaneously. The within-and between-cluster matrices are computed from unsupervised clustered data. The within-cluster scatter matrix contributes to encoding the possible variations in intraclasses and the between-cluster scatter matrix is useful for separating extra classes. The contributions are inversely proportional to the number of training samples per class. The advantages of the proposed method become more remarkable as the number of training samples per class decreases. Experimental results on the AR and Feret face databases demonstrate the effectiveness of the proposed method. |
文章类型 | Article |
关键词 | Dimensionality Reduction Face Recognition Feature Extraction Linear Discriminant Analysis (Lda) |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TNNLS.2014.2306844 |
收录类别 | SCI ; EI |
关键词[WOS] | LINEAR DISCRIMINANT-ANALYSIS ; FACE-RECOGNITION ; FEATURE-EXTRACTION ; DIMENSIONALITY REDUCTION ; CLASSIFICATION ; ALGORITHMS ; SELECTION |
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:000345518900006 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/22417 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Pang, Yanwei,Wang, Shuang,Yuan, Yuan. Learning Regularized LDA by Clustering[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2014,25(12):2191-2201. |
APA | Pang, Yanwei,Wang, Shuang,&Yuan, Yuan.(2014).Learning Regularized LDA by Clustering.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,25(12),2191-2201. |
MLA | Pang, Yanwei,et al."Learning Regularized LDA by Clustering".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 25.12(2014):2191-2201. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Learning Regularized(1962KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 |
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