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Iterative linear regression classification for image recognition
Feng Q.; Zhou Y.
Conference NameIEEE International Conference on Acoustics, Speech, and Signal Processing
Source PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Conference DateMAR 20-25, 2016
Conference PlaceShanghai, PEOPLES R CHINA

Traditional linear regression classification (LRC) suffers from a small sample size problem that the limited training samples of each class cannot comprehensively reflect different variations of the class. To address the problem, this paper proposes a novel iterative linear regression classification (ILRC) for image recognition. Different from traditional LRC, ILRC not only generates several new subspaces in each iteration but also uses the discrimination idea to optimize the training-set and testing samples. Extensive experiments on five benchmark databases demonstrate that the proposed ILRC classifier achieves better recognition performance than the traditional LRC and several state-of-the-art methods.

KeywordFace Recognition Linear Regression Object Recognition
URLView the original
Indexed BySCI
WOS Research AreaAcoustics ; Engineering
WOS SubjectAcoustics ; Engineering, Electrical & Electronic
WOS IDWOS:000388373401142
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionFaculty of Science and Technology
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Feng Q.,Zhou Y.. Iterative linear regression classification for image recognition[C],2016:1566-1570.
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