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Volumn , Issue , 2012, Pages 189-199

Heterogeneous datasets representation and learning using diffusion maps and Laplacian pyramids

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DIFFUSION; GEOMETRY; HARMONIC ANALYSIS; LAPLACE TRANSFORMS; LEARNING SYSTEMS; NUMBER THEORY;

EID: 84874021737     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972825.17     Document Type: Conference Paper
Times cited : (53)

References (22)
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    • Laplacian eigenmaps for dimensionality reduction and data representation
    • M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation, vol. 15 (2003), pp. 1373-1396.
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    • Belkin, M.1    Niyogi, P.2
  • 8
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    • Semi-supervised learning on riemannian manifolds
    • M. Belkin and P. Niyogi, Semi-supervised learning on riemannian manifolds, Machine Learning, vol. 56 (2004), pp. 209-239.
    • (2004) Machine Learning , vol.56 , pp. 209-239
    • Belkin, M.1    Niyogi, P.2
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    • Uber die praktische auosung von integralgleichungen mit anwendungen auf randwertaufgaben
    • E. J. Nystrom, Uber die praktische auosung von integralgleichungen mit anwendungen auf randwertaufgaben, Acta Mathematica, (1930), pp. 185-204, .
    • (1930) Acta Mathematica , pp. 185-204
    • Nystrom, E.J.1
  • 16
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    • Using the Nystrom method to speed up kernel machines
    • C. Williams and M. Seeger, Using the Nystrom method to speed up kernel machines, Neural Inf. Process. Systems, vol. 13 (2001), pp. 682-688.
    • (2001) Neural Inf. Process. Systems , vol.13 , pp. 682-688
    • Williams, C.1    Seeger, M.2
  • 17
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    • Efficient spatiotemporal grouping using the Nystrom method
    • Vision Pattern Recognition
    • C. Fowlkes, S. Belongie and J. Malik, Efficient spatiotemporal grouping using the Nystrom method, Proc. of IEEE Comput. Vision Pattern Recognition, (2001).
    • (2001) Proc. of IEEE Comput
    • Fowlkes, C.1    Belongie, S.2    Malik, J.3
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    • http://archive.ics.uci.edu/ml/datasets.
  • 22
    • 72049084727 scopus 로고    scopus 로고
    • Modeling wine preferences by data mining from physicochemical properties
    • P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis, Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems, vol. 47 (2009), pp. 547-553.
    • (2009) Decision Support Systems , vol.47 , pp. 547-553
    • Cortez, P.1    Cerdeira, A.2    Almeida, F.3    Matos, T.4    Reis, J.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.