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Volumn 15, Issue , 2011, Pages 51-59

Spectral dimensionality reduction via maximum entropy

Author keywords

[No Author keywords available]

Indexed keywords

DIMENSIONALITY REDUCTION; GAUSSIAN RANDOM FIELDS; HUMAN MOTION CAPTURE DATA; LOCALLY LINEAR EMBEDDING; MAXIMUM ENTROPY; MAXIMUM ENTROPY PRINCIPLE; MAXIMUM VARIANCE; NONLINEAR GENERALIZATIONS; PARAMETER FITTING; PROBABILISTIC MODELS; ROBOT NAVIGATION;

EID: 84862286992     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper
Times cited : (18)

References (19)
  • 1
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • doi: 10.1162/089976603321780317
    • Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003. doi: 10.1162/089976603321780317.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 7
    • 0002945580 scopus 로고
    • Bayesian methods: General background
    • J. H. Justice, editor. Cambridge University Press
    • Edwin T. Jaynes. Bayesian methods: General background. In J. H. Justice, editor, Maximum Entropy and Bayesian Methods in Applied Statistics, pages 1-25. Cambridge University Press, 1986.
    • (1986) Maximum Entropy and Bayesian Methods in Applied Statistics , pp. 1-25
    • Jaynes, E.T.1
  • 10
    • 84898980901 scopus 로고    scopus 로고
    • Gaussian process models for visualisation of high dimensional data
    • Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Cambridge, MA. MIT Press
    • Neil D. Lawrence. Gaussian process models for visualisation of high dimensional data. In Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Advances in Neural Information Processing Systems, volume 16, pages 329-336, Cambridge, MA, 2004. MIT Press.
    • (2004) Advances in Neural Information Processing Systems , vol.16 , pp. 329-336
    • Lawrence, N.D.1
  • 11
    • 27844605876 scopus 로고    scopus 로고
    • Probabilistic non-linear principal component analysis with Gaussian process latent variable models
    • 11
    • Neil D. Lawrence. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research, 6:1783-1816, 11 2005.
    • (2005) Journal of Machine Learning Research , vol.6 , pp. 1783-1816
    • Lawrence, N.D.1
  • 13
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • doi: 10.1126/science.290.5500.2323
    • Sam T. Roweis and Lawrence K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000. doi: 10.1126/science.290.5500.2323.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 14
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • doi: 10.1162/089976698300017467
    • Bernhard Schölkopf, Alexander Smola, and Klaus- Robert Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299-1319, 1998. doi: 10.1162/089976698300017467.
    • (1998) Neural Computation , vol.10 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3
  • 15
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • doi: 10.1126/science.290.5500.2319
    • Joshua B. Tenenbaum, Virginia de Silva, and John C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500): 2319-2323, 2000. doi: 10.1126/science.290.5500.2319.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 16
    • 10644295905 scopus 로고    scopus 로고
    • Springer-Verlag, New York. ISBN 9780387402727
    • Larry A. Wasserman. All of Statistics. Springer- Verlag, New York, 2003. ISBN 9780387402727.
    • (2003) All of Statistics
    • Wasserman, L.A.1
  • 17
    • 14344251006 scopus 로고    scopus 로고
    • Learning a kernel matrix for nonlinear dimensionality reduction
    • Kilian Q. Weinberger, Fei Sha, and Lawrence K. Saul. Learning a kernel matrix for nonlinear dimensionality reduction. In Greiner and Schuurmans [2004], pages 839-846.
    • (2004) Greiner and Schuurmans , pp. 839-846
    • Weinberger, K.Q.1    Sha, F.2    Saul, L.K.3
  • 18
    • 84898939890 scopus 로고    scopus 로고
    • On a connection between kernel PCA and metric multidimensional scaling
    • Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Cambridge, MA. MIT Press
    • Christopher K. I. Williams. On a connection between kernel PCA and metric multidimensional scaling. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Advances in Neural Information Processing Systems, volume 13, pages 675-681, Cambridge, MA, 2001. MIT Press.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 675-681
    • Williams, C.K.I.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.