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Volumn 57, Issue 1-4, 2004, Pages 493-499

Relative gradient speeding up additive updates for nonnegative matrix factorization

Author keywords

Additive updates; Multiplicative updates; Nonnegative matrix factorization; Relative gradient

Indexed keywords

BLIND SOURCE SEPARATION; CONVERGENCE OF NUMERICAL METHODS; ERROR ANALYSIS; INDEPENDENT COMPONENT ANALYSIS; OPTIMIZATION;

EID: 1642488120     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2004.01.002     Document Type: Article
Times cited : (7)

References (20)
  • 1
    • 0000396062 scopus 로고    scopus 로고
    • Natural gradient works efficiently in learning
    • Amari S. Natural gradient works efficiently in learning Neural Comput. 10 1998 251-276
    • (1998) Neural Comput. , vol.10 , pp. 251-276
    • Amari, S.1
  • 2
    • 0036952875 scopus 로고    scopus 로고
    • Blind signal separation and independent component analysis
    • Amari S.-I. Hyvarinen A. Lee S.-Y. et-al. Blind signal separation and independent component analysis Neurocomputing 49 2002 1-5
    • (2002) Neurocomputing , vol.49 , pp. 1-5
    • Amari, S.-I.1    Hyvarinen, A.2    Lee, S.-Y.3
  • 3
    • 0003372875 scopus 로고    scopus 로고
    • Methods of information geometry
    • American Mathematical Society and Oxford University Press
    • S. Amari, H. Nagaoka, Methods of information geometry, Translations of Mathematical Monographs, Vol. 191, American Mathematical Society and Oxford University Press, 2000.
    • (2000) Translations of Mathematical Monographs , vol.191
    • Amari, S.1    Nagaoka, H.2
  • 4
    • 34447266299 scopus 로고    scopus 로고
    • Blind signal separation: Statistical principles, special issue on blind identification and estimation
    • R.-W. Liu, L. Tong (Eds.)
    • J.-F. Cardoso, Blind signal separation: statistical principles, special issue on blind identification and estimation, in: R.-W. Liu, L. Tong (Eds.), Proc. IEEE 90 (1998) 2009-2026.
    • (1998) Proc. IEEE , vol.90 , pp. 2009-2026
    • Cardoso, J.-F.1
  • 6
    • 1642523939 scopus 로고    scopus 로고
    • CBCL Face Database #1, MIT Center For Biological and Computation Learning, available online, see
    • CBCL Face Database #1, MIT Center For Biological and Computation Learning, available online, see http://www.ai.mit.edu/projects/cbcl.
  • 7
    • 0028416938 scopus 로고
    • Independent component analysis: A new concept?
    • Comon P. Independent component analysis: a new concept? Signal Process. 36 1994 287-314
    • (1994) Signal Process. , vol.36 , pp. 287-314
    • Comon, P.1
  • 8
    • 0002515425 scopus 로고    scopus 로고
    • Natural gradient adaptation
    • S. Haykin (Ed.), New York: Wiley
    • Douglas S.C. Amari S. Natural gradient adaptation, in: Haykin S. (Ed.), Unsupervised Adaptive Filtering Blind Source Separation Vol. 1 2000 13-61 Wiley New York
    • (2000) Unsupervised Adaptive Filtering , vol.1 , pp. 13-61
    • Douglas, S.C.1    Amari, S.2
  • 9
    • 84857841726 scopus 로고    scopus 로고
    • Non-negative sparse coding
    • Proceedings of IEEE Workshop on Neural Networks for Signal Processing 2002, Martigny, Switzerland
    • P.O. Hoyer, Non-negative sparse coding, in: Neural Networks for Signal Processing XII, Proceedings of IEEE Workshop on Neural Networks for Signal Processing 2002, Martigny, Switzerland, pp. 557-565.
    • (2002) Neural Networks for Signal Processing XII , pp. 557-565
    • Hoyer, P.O.1
  • 10
    • 0037780988 scopus 로고    scopus 로고
    • Modeling receptive fields with non-negative sparse coding
    • Hoyer P.O. Modeling receptive fields with non-negative sparse coding Neurocomputing 52-54 2003 547-552
    • (2003) Neurocomputing , vol.52-54 , pp. 547-552
    • Hoyer, P.O.1
  • 11
    • 0036284161 scopus 로고    scopus 로고
    • A multi-layer sparse coding network learns contour coding from natural images
    • Hoyer P.O. Hyvärinen A. A multi-layer sparse coding network learns contour coding from natural images Vision Res. 42 2002 1593-1605
    • (2002) Vision Res. , vol.42 , pp. 1593-1605
    • Hoyer, P.O.1    Hyvärinen, A.2
  • 12
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects with nonnegative matrix factorization
    • Lee D.D. Seung H.S. Learning the parts of objects with nonnegative matrix factorization Nature 401 1999 788-791
    • (1999) Nature , vol.401 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 13
    • 84898964201 scopus 로고    scopus 로고
    • Algorithms for nonnegative matrix factorization
    • T. Leen, T. Dietterich, V. Tresp (Eds.), MIT Press, Cambridge, MA, November
    • D.D. Lee, H.S. Seung, Algorithms for nonnegative matrix factorization, in: T. Leen, T. Dietterich, V. Tresp (Eds.), Advances in Neural Information Processing Systems, Vol. 13, MIT Press, Cambridge, MA, November 2001, pp. 556-562.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 556-562
    • Lee, D.D.1    Seung, H.S.2
  • 14
    • 0029938380 scopus 로고    scopus 로고
    • Emergence of simple-cell receptive field properties by learning a sparse code for natural images
    • Olshausen B.A. Field D.J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images Nature 381 1996 607-609
    • (1996) Nature , vol.381 , pp. 607-609
    • Olshausen, B.A.1    Field, D.J.2
  • 15
    • 0028561099 scopus 로고
    • Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values
    • Paatero P. Tapper U. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values Environmetrics 5 1994 111-126
    • (1994) Environmetrics , vol.5 , pp. 111-126
    • Paatero, P.1    Tapper, U.2
  • 16
    • 0038460232 scopus 로고    scopus 로고
    • Algorithms for non-negative independent component analysis
    • Plumbley M.D. Algorithms for non-negative independent component analysis IEEE Trans. Neural Networks 14 2003 534-543
    • (2003) IEEE Trans. Neural Networks , vol.14 , pp. 534-543
    • Plumbley, M.D.1
  • 17
    • 84898965722 scopus 로고    scopus 로고
    • Multiplicative updates for classification by mixture models
    • T.G. Dietterich, S. Becker, Z. Ghahramani (Eds.), MIT Press, Cambridge, MA, December
    • L.K. Saul, D.D. Lee, Multiplicative updates for classification by mixture models, in: T.G. Dietterich, S. Becker, Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems, Vol. 14, MIT Press, Cambridge, MA, December 2002, pp. 897-904.
    • (2002) Advances in Neural Information Processing Systems , vol.14 , pp. 897-904
    • Saul, L.K.1    Lee, D.D.2
  • 18
    • 26744451702 scopus 로고    scopus 로고
    • Review on nonnegative matrix factorization
    • Technical Report, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
    • Weixiang Liu, Nanning Zheng, Review on nonnegative matrix factorization, Technical Report, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, 2003.
    • (2003)
    • Weixiang, L.1    Nanning, Z.2
  • 19
    • 0347478104 scopus 로고    scopus 로고
    • Learning sparse features for classification by mixture models
    • Liu Weixiang Zheng Nanning Learning sparse features for classification by mixture models Pattern Recognition Lett. 25 2004 155-161
    • (2004) Pattern Recognition Lett. , vol.25 , pp. 155-161
    • Liu, W.1    Zheng, N.2
  • 20
    • 1642564819 scopus 로고    scopus 로고
    • Multiplicative updating rule for blind separation derived from the method of scoring
    • M.J. Jordan, M. Kearns, S.A. Solla (Eds.), MIT Press, Cambridge, MA, December
    • H.H. Yang, Multiplicative updating rule for blind separation derived from the method of scoring, in: M.J. Jordan, M. Kearns, S.A. Solla (Eds.), Advances in Neural Information Processing Systems, Vol. 10, MIT Press, Cambridge, MA, December 1998, pp. 696-702.
    • (1998) Advances in Neural Information Processing Systems , vol.10 , pp. 696-702
    • Yang, H.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.