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Volumn 23, Issue 12, 2011, Pages 3287-3302

On the relation of slow feature analysis and Laplacian eigenmaps

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; HUMAN; LETTER; METHODOLOGY; NERVE CELL; NERVE TRACT; PATTERN RECOGNITION; PHYSIOLOGY; TIME; VISUAL CORTEX;

EID: 84856370602     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00214     Document Type: Letter
Times cited : (64)

References (22)
  • 1
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373-1396.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 2
    • 3142725535 scopus 로고    scopus 로고
    • Semi-supervised learning on Riemannian manifolds
    • Belkin, M., & Niyogi, P. (2004). Semi-supervised learning on Riemannian manifolds. Machine Learning, 56(1), 209-239.
    • (2004) Machine Learning , vol.56 , Issue.1 , pp. 209-239
    • Belkin, M.1    Niyogi, P.2
  • 3
    • 55449104028 scopus 로고    scopus 로고
    • Towards a theoretical foundation for Laplacian-based manifold methods
    • Belkin, M., & Niyogi, P. (2008). Towards a theoretical foundation for Laplacian-based manifold methods. Journal of Computer and System Sciences, 74(8), 1289-1308.
    • (2008) Journal of Computer and System Sciences , vol.74 , Issue.8 , pp. 1289-1308
    • Belkin, M.1    Niyogi, P.2
  • 5
    • 27244444336 scopus 로고    scopus 로고
    • Slow feature analysis yields a rich repertoire of complex cells
    • Berkes, P., & Wiskott, L. (2005). Slow feature analysis yields a rich repertoire of complex cells. Journal of Vision, 5(6), 579-602.
    • (2005) Journal of Vision , vol.5 , Issue.6 , pp. 579-602
    • Berkes, P.1    Wiskott, L.2
  • 6
    • 65549124370 scopus 로고    scopus 로고
    • The first order asymptotics of the extreme eigenvectors of certain Hermitian Toeplitz matrices
    • Böttcher, A., Grudsky, S., Maksimenko, E., & Unterberger, J. (2009). The first order asymptotics of the extreme eigenvectors of certain Hermitian Toeplitz matrices. Integral Equations and Operator Theory, 63(2), 165-180.
    • (2009) Integral Equations and Operator Theory , vol.63 , Issue.2 , pp. 165-180
    • Böttcher, A.1    Grudsky, S.2    Maksimenko, E.3    Unterberger, J.4
  • 7
    • 77955655063 scopus 로고    scopus 로고
    • Semi-supervised learning in gigantic image collections
    • Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culote (Eds.), Cambridge, MA: MIT Press
    • Fergus, R., Weiss, Y., & Torralba, A. (2009). Semi-supervised learning in gigantic image collections. In Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, & A. Culote (Eds.), Advances in neural information processing systems, 22 (pp. 522-530). Cambridge, MA: MIT Press.
    • (2009) Advances in neural information processing systems , vol.22 , pp. 522-530
    • Fergus, R.1    Weiss, Y.2    Torralba, A.3
  • 8
    • 34548412214 scopus 로고    scopus 로고
    • Slowness and sparseness lead to place, head-direction, and spatial-view cells
    • Franzius, M., Sprekeler, H., & Wiskott, L. (2007). Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Computational Biology, 3(8), e166.
    • (2007) PLoS Computational Biology , vol.3 , Issue.8
    • Franzius, M.1    Sprekeler, H.2    Wiskott, L.3
  • 11
    • 78649682278 scopus 로고    scopus 로고
    • A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction
    • Klampfl, S., & Maass, W. (2010). A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction. Neural Computation, 22(12), 2979-3035.
    • (2010) Neural Computation , vol.22 , Issue.12 , pp. 2979-3035
    • Klampfl, S.1    Maass, W.2
  • 12
    • 78049417739 scopus 로고    scopus 로고
    • Reinforcement learning on slow features of high-dimensional input streams
    • Legenstein, R., Wilbert, N., & Wiskott, L. (2010). Reinforcement learning on slow features of high-dimensional input streams. Plos Computational Biology, 6(8).
    • (2010) Plos Computational Biology , vol.6 , Issue.8
    • Legenstein, R.1    Wilbert, N.2    Wiskott, L.3
  • 13
    • 35748957806 scopus 로고    scopus 로고
    • Proto-value functions: A Laplacian framework for learning representation and control in Markov decision processes
    • Mahadevan, S., & Maggioni, M. (2007). Proto-value functions: A Laplacian framework for learning representation and control in Markov decision processes. Journal of Machine Learning Research, 8, 2169-2231.
    • (2007) Journal of Machine Learning Research , vol.8 , pp. 2169-2231
    • Mahadevan, S.1    Maggioni, M.2
  • 14
    • 84899013108 scopus 로고    scopus 로고
    • On spectral clustering: Analysis and an algorithm
    • T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Cambridge, MA: MIT Press
    • Ng, A., Jordan, A., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14. Cambridge, MA: MIT Press.
    • (2002) Advances in neural information processing systems, 14
    • Ng, A.1    Jordan, A.2    Weiss, Y.3
  • 16
    • 48349109325 scopus 로고    scopus 로고
    • Non-linear independent component analysis with diffusion maps
    • Singer, A., & Coifman, R. (2008). Non-linear independent component analysis with diffusion maps. Applied and Computational Harmonic Analysis, 25(2), 226-239.
    • (2008) Applied and Computational Harmonic Analysis , vol.25 , Issue.2 , pp. 226-239
    • Singer, A.1    Coifman, R.2
  • 19
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395-416.
    • (2007) Statistics and Computing , vol.17 , Issue.4 , pp. 395-416
    • von Luxburg, U.1
  • 20
    • 0041324871 scopus 로고    scopus 로고
    • Slow feature analysis: A theoretical analysis of optimal free responses
    • Wiskott, L. (2003). Slow feature analysis: A theoretical analysis of optimal free responses. Neural Computation, 15(9), 2147-2177.
    • (2003) Neural Computation , vol.15 , Issue.9 , pp. 2147-2177
    • Wiskott, L.1
  • 21
    • 0036546660 scopus 로고    scopus 로고
    • Slow feature analysis: Unsupervised learning of invariances
    • Wiskott, L., & Sejnowski, T. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14, 715-770.
    • (2002) Neural Computation , vol.14 , pp. 715-770
    • Wiskott, L.1    Sejnowski, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.