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Volumn , Issue , 2009, Pages 862-870

Regularized distance metric learning: Theory and algorithm

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; LEARNING ALGORITHMS;

EID: 84863337530     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (187)

References (19)
  • 8
    • 24744436360 scopus 로고    scopus 로고
    • Think globally, fit locally: Unsupervised learning of low dimensional manifolds
    • L. K. Saul and S. T. Roweis. Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research, 4, 2003.
    • Journal of Machine Learning Research , vol.4 , pp. 2003
    • Saul, L.K.1    Roweis, S.T.2
  • 11
    • 0003621102 scopus 로고
    • An introduction to the conjugate gradient method without the agonizing pain
    • Pittsburgh, PA, USA
    • Jonathan R Shewchuk. An introduction to the conjugate gradient method without the agonizing pain. Technical report, Carnegie Mellon University, Pittsburgh, PA, USA, 1994.
    • (1994) Technical Report, Carnegie Mellon University
    • Shewchuk, J.R.1
  • 13
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • J.B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2000.
    • Science , vol.290 , pp. 2000
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.