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Volumn 4403 LNCS, Issue , 2007, Pages 772-787

Non-linear dimensionality reduction procedures for certain large-dimensional multi-objective optimization problems: Employing correntropy and a novel maximum variance unfolding

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EFFICIENCY; EVOLUTIONARY ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS;

EID: 37249034215     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-70928-2_58     Document Type: Conference Paper
Times cited : (107)

References (18)
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    • Deb, K., Saxena, D.: Searching for pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: IEEE Congress on Evolutionary Computation. (2006) 3353-3360
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    • Deb, K.1    Saxena, D.2
  • 3
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    • Weinberger, K.Q., Saul, L.K.: An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In: In Proceedings of the National Conference on Artificial Intelligence (AAAI), Nectar paper. (2006)
    • Weinberger, K.Q., Saul, L.K.: An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In: In Proceedings of the National Conference on Artificial Intelligence (AAAI), Nectar paper. (2006)
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    • Saul, L.K., Weinberger, K.Q., Ham, J.H., Sha, F., Lee, D.D.: Spectral methods for dimensionality reduction. In: Semisupervised Learning (to appear) (smdr.ssl06.pdf at http://www.cs.ucsd.edu/saul/abstracts.html). MIT Press, Cambridge, MA (2006)
    • Saul, L.K., Weinberger, K.Q., Ham, J.H., Sha, F., Lee, D.D.: Spectral methods for dimensionality reduction. In: Semisupervised Learning (to appear) (smdr.ssl06.pdf at http://www.cs.ucsd.edu/saul/abstracts.html). MIT Press, Cambridge, MA (2006)
  • 9
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum, J.B., de Silva, V., Langford, J.C: A global geometric framework for nonlinear dimensionality reduction. Science 290 (2000) 2319-2323
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.B.1    de Silva, V.2    Langford, J.C.3
  • 10
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15 (2003) 1373-1396
    • (2003) Neural Computation , vol.15 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 11
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000)2323-2326
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.1    Saul, L.2
  • 12
    • 4344609668 scopus 로고    scopus 로고
    • A kernel view of the dimensionality reduction of manifolds
    • TR-110, Max Planck Institute for Biological Cybernatics, Tubingen, Germany
    • Ham, J.H., Lee, D.D., Mika, S.: A kernel view of the dimensionality reduction of manifolds. Technical Reports TR-110, Max Planck Institute for Biological Cybernatics, Tubingen, Germany (2003)
    • (2003) Technical Reports
    • Ham, J.H.1    Lee, D.D.2    Mika, S.3
  • 16
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    • Nonlinear component analysis as a kernel eigenvalue problem
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    • Scalable test problems for evolutionary multi-objective optimization
    • CEC
    • Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Congress on Evolutionary Computation (CEC). Volume 1. (2002) 825-830
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.