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Volumn , Issue , 2010, Pages

Unsupervised kernel dimension reduction

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

EID: 85162068491     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (38)

References (30)
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    • Nonlinear dimensionality reduction by locally linear embedding
    • S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323, 2000.
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    • Roweis, S.T.1    Saul, L.K.2
  • 2
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • J. B. Tenenbaum, V. Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319, 2000.
    • (2000) Science , vol.290 , pp. 2319
    • Tenenbaum, J.B.1    Silva, V.2    Langford, J.C.3
  • 4
    • 84898980901 scopus 로고    scopus 로고
    • Gaussian process latent variable models for visualisation of high dimensional data
    • MIT Press
    • N. D. Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. In Advances in Neural Information Processing Systems 16, pages 329-336. MIT Press, 2004.
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 329-336
    • Lawrence, N.D.1
  • 5
    • 0012657603 scopus 로고    scopus 로고
    • Dimension reduction and visualization in discriminant analysis (with discussion)
    • R. D. Cook and X. Yin. Dimension reduction and visualization in discriminant analysis (with discussion). Australian & New Zealand Journal of Statistics, 43:147-199, 2001.
    • (2001) Australian & New Zealand Journal of Statistics , vol.43 , pp. 147-199
    • Cook, R.D.1    Yin, X.2
  • 9
    • 0001659464 scopus 로고
    • On principal Hessian directions for data visualization and dimension reduction: Another application of Stein's lemma
    • K.-C. Li. On principal Hessian directions for data visualization and dimension reduction: another application of Stein's lemma. Journal of the American Statistical Association, 86:316-342, 1992.
    • (1992) Journal of the American Statistical Association , vol.86 , pp. 316-342
    • Li, K.-C.1
  • 21
    • 4544371135 scopus 로고    scopus 로고
    • Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces
    • K. Fukumizu, F. R. Bach, and M. I. Jordan. Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. The Journal of Machine Learning Research, 5:73-99, 2004.
    • (2004) The Journal of Machine Learning Research , vol.5 , pp. 73-99
    • Fukumizu, K.1    Bach, F.R.2    Jordan, M.I.3
  • 22
    • 73349108769 scopus 로고    scopus 로고
    • Sufficient dimension reduction and prediction in regression
    • K. P. Adragni and R. D. Cook. Sufficient dimension reduction and prediction in regression. Philosophical Transactions A, 367:4385-4405, 2009.
    • (2009) Philosophical Transactions A , vol.367 , pp. 4385-4405
    • Adragni, K.P.1    Cook, R.D.2
  • 24
    • 0000704059 scopus 로고    scopus 로고
    • Computation with infinite neural networks
    • C. K. I.Williams. Computation with infinite neural networks. Neural Computation, 10:1203-1216, 1998.
    • (1998) Neural Computation , vol.10 , pp. 1203-1216
    • Williams, C.K.I.1
  • 28
    • 0032216898 scopus 로고    scopus 로고
    • The geometry of algorithms with orthogonality constraints
    • A. Edelman, T. A. Arias, and S. T. Smith. The geometry of algorithms with orthogonality constraints. SIAM J. Matrix Anal. Appl, 20:303-353, 1998.
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    • Edelman, A.1    Arias, T.A.2    Smith, S.T.3
  • 30
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    • Distancemetric learning for large margin nearest neighbor classification
    • K. Q.Weinberger and L. K. Saul. Distancemetric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research, 10:207-244, 2009.
    • (2009) The Journal of Machine Learning Research , vol.10 , pp. 207-244
    • Weinberger, K.Q.1    Saul, L.K.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.