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Ensemble learning for independent component analysis
Cheng, J; Liu, QS; Lu, HQ; Chen, YW
2006
发表期刊PATTERN RECOGNITION
卷号39期号:1页码:81-88
文章类型Article
摘要It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased. In this paper, we propose an enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems. Firstly, we use the random resampling technique to generate some low dimensional feature subspaces, and one classifier is constructed in each feature subspace. Then these classifiers are combined into an ensemble classifier using a final decision rule. Extensive experimentations performed on the FERET database suggest that the proposed method can improve the performance of ICA classifier. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
关键词Independent Component Analysis Ensemble Learning Random Independent Subspace Face Recognition Majority Voting
WOS标题词Science & Technology ; Technology
关键词[WOS]BLIND SEPARATION ; FACE-RECOGNITION ; EIGENFACES ; ALGORITHM
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000233222700007
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/9354
专题09年以前成果
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Nokia Res Ctr, Beijing 100013, Peoples R China
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Cheng, J,Liu, QS,Lu, HQ,et al. Ensemble learning for independent component analysis[J]. PATTERN RECOGNITION,2006,39(1):81-88.
APA Cheng, J,Liu, QS,Lu, HQ,&Chen, YW.(2006).Ensemble learning for independent component analysis.PATTERN RECOGNITION,39(1),81-88.
MLA Cheng, J,et al."Ensemble learning for independent component analysis".PATTERN RECOGNITION 39.1(2006):81-88.
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