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Volumn , Issue , 2005, Pages

Proximity graphs for clustering and manifold Learning

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS;

EID: 84898939894     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (74)

References (13)
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    • Wu, Z.1    Leahy, R.2
  • 3
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    • Efficient graph-based image segmentation
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    • Pedro F. Felzenszwalb and Daniel P. Huttenlocher. Efficient graph-based image segmentation. Int. J. Computer Vision, 59(2):167-181, September 2004.
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  • 4
    • 14344265724 scopus 로고    scopus 로고
    • Learning to cluster using local neighborhood structure
    • Romer Rosales, Kannan Achan, and Brendan Frey. Learning to cluster using local neighborhood structure. In ICML, 2004.
    • (2004) ICML
    • Rosales, R.1    Achan, K.2    Frey, B.3
  • 5
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • December 22
    • Joshua B. Tenenbaum, Vin de Silva, and John C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, December 22 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 6
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • December 22
    • Sam T. Roweis and Lawrence K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, December 22 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 7
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • June
    • Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, June 2003.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 8
    • 5044226695 scopus 로고    scopus 로고
    • Unsupervised learning of image manifolds by semidefinite programming
    • Kilian Q. Weinberger and Lawrence K. Saul. Unsupervised learning of image manifolds by semidefinite programming. In CVPR, 2004.
    • (2004) CVPR
    • Weinberger, K.Q.1    Saul, L.K.2
  • 9
    • 0000902522 scopus 로고    scopus 로고
    • Data clustering using a model granular magnet
    • November
    • Marcelo Blatt, Shai Wiseman, and Eytan Domany. Data clustering using a model granular magnet. Neural Computation, 9(8):1805-1842, November 1997.
    • (1997) Neural Computation , vol.9 , Issue.8 , pp. 1805-1842
    • Blatt, M.1    Wiseman, S.2    Domany, E.3
  • 10
    • 0014976008 scopus 로고
    • Graph-theoretical methods for detecting and describing gestalt clusters
    • April
    • C. T. Zahn. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Computers, C-20(1):68-86, April 1971.
    • (1971) IEEE Trans. Computers , vol.C-20 , Issue.1 , pp. 68-86
    • Zahn, C.T.1
  • 11
    • 51949107738 scopus 로고    scopus 로고
    • Eigencuts: Half-lives of eigenflows for spectral clustering
    • Chakra Chennubhotla and Allan Jepson. EigenCuts: Half-lives of EigenFlows for spectral clustering. In NIPS, 2003.
    • (2003) NIPS
    • Chennubhotla, C.1    Jepson, A.2
  • 13
    • 0035481858 scopus 로고    scopus 로고
    • Self organization in vision: Stochastic clustering for image segmentation, perceptual grouping, and image database organization
    • October
    • Yoram Gdalyahu, Daphna Weinshall, and Michael Werman. Self organization in vision: Stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Trans. on Pattern Anal. and Machine Intel., 23(10):1053-1074, October 2001.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.