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Volumn 5, Issue , 2009, Pages 575-582

Non-negative semi-supervised learning

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMIC PROPERTIES; ARBITRARY ORDER; CLASSIFICATION POWER; CONVERGENCY; DISCRIMINATING POWER; PARTIAL OCCLUSIONS; SEMI-SUPERVISED LEARNING; STATE-OF-THE-ART ALGORITHMS; UNLABELED DATA;

EID: 84862297606     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (12)

References (19)
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  • 2
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  • 3
    • 36448962045 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from examples
    • Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from examples. JMLR.
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    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 4
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    • Semi-supervised discriminant analysis
    • Cai, D., He, X., & Han, J. (2007). Semi-supervised discriminant analysis. ICCV.
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    • Cai, D.1    He, X.2    Han, J.3
  • 5
    • 33745944718 scopus 로고    scopus 로고
    • Sparse image coding using a 3D non-negative tensor factorization
    • Hazan, T., Polak, S., & Shashua, A. (2005). Sparse image coding using a 3d non-negative tensor factorization. ICCV, 1, 50-57.
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    • Hazan, T.1    Polak, S.2    Shashua, A.3
  • 7
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    • A novel discriminant non-negative matrix factorization algorithm with applications to facial image characterization problems
    • Kotsia, I., Zafeiriou, S., & Pitas, I. (2007). A novel discriminant non-negative matrix factorization algorithm with applications to facial image characterization problems. TIFS, 588-595.
    • (2007) TIFS , pp. 588-595
    • Kotsia, I.1    Zafeiriou, S.2    Pitas, I.3
  • 9
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by nonnegative matrix factorization
    • Lee, D., & Seung, H. (1999). Learning the parts of objects by nonnegative matrix factorization. Nature.
    • (1999) Nature
    • Lee, D.1    Seung, H.2
  • 10
    • 0035683536 scopus 로고    scopus 로고
    • Learning spatially localized, parts-based representation
    • Li, S., Hou, X., Zhang, H., & Cheng, Q. (2001). Learning spatially localized, parts-based representation. CVPR.
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    • Li, S.1    Hou, X.2    Zhang, H.3    Cheng, Q.4
  • 11
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    • Non-negative tensor factorization with applications to statistics and computer vision
    • Shashua, A., & Hazan, T. (2005). Non-negative tensor factorization with applications to statistics and computer vision. ICML.
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  • 12
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  • 13
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.