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Volumn , Issue , 2007, Pages

Classification of weakly-labeled data with partial equivalence relations

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS; COMPUTER VISION; FACE RECOGNITION; IMAGE PROCESSING; IMAGE RETRIEVAL; LABELING; LABELS; LEARNING SYSTEMS; SET THEORY;

EID: 50649124023     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2007.4409047     Document Type: Conference Paper
Times cited : (10)

References (18)
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  • 3
    • 0034300875 scopus 로고    scopus 로고
    • A new LDA-based face recognition system which can solve the small sample size problem
    • L. F. Chen, H. Y. M. Liao, J. C. Lin, M. T. Ko, and G. J. Yu. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition, 33(10), 2000.
    • (2000) Pattern Recognition , vol.33 , Issue.10
    • Chen, L.F.1    Liao, H.Y.M.2    Lin, J.C.3    Ko, M.T.4    Yu, G.J.5
  • 6
    • 5044229207 scopus 로고    scopus 로고
    • T. Hertz, A. Bar-Hillel, and D. Weinshall. Learning distance functions for image retrieval. In Proc. IEEE Conf on Computer Vision and Pattern Recognition (CVPR), 2004.
    • T. Hertz, A. Bar-Hillel, and D. Weinshall. Learning distance functions for image retrieval. In Proc. IEEE Conf on Computer Vision and Pattern Recognition (CVPR), 2004.
  • 7
    • 33751565280 scopus 로고    scopus 로고
    • R. Huang, Q. S. Liu, H. Q. Lu, and S. D. Ma. Solving small sample size problem of LDA. In Proc. Int. Conf. on Pattern Recognition (ICPR), 3:29-32, 2002.
    • R. Huang, Q. S. Liu, H. Q. Lu, and S. D. Ma. Solving small sample size problem of LDA. In Proc. Int. Conf. on Pattern Recognition (ICPR), 3:29-32, 2002.
  • 9
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    • Nonlinear dimensionality reduction by locally linear embedding
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    • Roweis, S.1    Saul, L.2
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    • N. Shental, T. Hertz, D. Weinshall, and M. Pavel. Adjustment learning and relevant component analysis. In Proc. European Conference on Computer Vision (ECCV), pages 776-790, 2002.
    • N. Shental, T. Hertz, D. Weinshall, and M. Pavel. Adjustment learning and relevant component analysis. In Proc. European Conference on Computer Vision (ECCV), pages 776-790, 2002.
  • 12
    • 33745963387 scopus 로고    scopus 로고
    • I. W. Tsang, P. M. Cheung, and J. T. Kwok. Kernel relevant component analysis for distance metric learning. In Proc. Int. Joint Conf. on Neural Networks (IJCNN), 2005.
    • I. W. Tsang, P. M. Cheung, and J. T. Kwok. Kernel relevant component analysis for distance metric learning. In Proc. Int. Joint Conf. on Neural Networks (IJCNN), 2005.
  • 13
    • 0026384289 scopus 로고    scopus 로고
    • M. A. Turk and A. P. Pentland. Face recognition using eigen-faces. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 586-591, 1991.
    • M. A. Turk and A. P. Pentland. Face recognition using eigen-faces. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 586-591, 1991.
  • 14
    • 50649112115 scopus 로고    scopus 로고
    • K. Wagstaff, C. Cardie, S. Rogers, and S. Scroedl. Constrained k-means clustering with background knowledge. In Proc. Int. Conf. on Machine Learning (ICML), 2001.
    • K. Wagstaff, C. Cardie, S. Rogers, and S. Scroedl. Constrained k-means clustering with background knowledge. In Proc. Int. Conf. on Machine Learning (ICML), 2001.
  • 16
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    • K. Q. Weinberger and L. K. Saul. Unsupervised learning of image manifolds by semidefinite programming. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 988-995, 2004.
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    • Computational and theoretical analysis of null space and orthogonal linear discriminant analysis
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.