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Ensemble learning for independent component analysis
Cheng, J; Liu, QS; Lu, HQ; Chen, YW
AbstractIt 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.
KeywordIndependent Component Analysis Ensemble Learning Random Independent Subspace Face Recognition Majority Voting
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000233222700007
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Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
2.Nokia Res Ctr, Beijing 100013, Peoples R China
Recommended Citation
GB/T 7714
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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