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Volumn 47, Issue 8, 2014, Pages 2569-2581

DANCo: An intrinsic dimensionality estimator exploiting angle and norm concentration

Author keywords

Intrinsic dimensionality estimation; Kullback Leibler divergence; Manifold learning; Nearest neighbor distance distribution; Von Mises distribution

Indexed keywords

SOFTWARE ENGINEERING;

EID: 84899486572     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2014.02.013     Document Type: Article
Times cited : (89)

References (59)
  • 6
    • 70350367551 scopus 로고    scopus 로고
    • A comparative evaluation of nonlinear dynamics methods for time series prediction
    • F. Camastra, and M. Filippone A comparative evaluation of nonlinear dynamics methods for time series prediction Neural Comput. Appl. 18 8 2009 1021 1029
    • (2009) Neural Comput. Appl. , vol.18 , Issue.8 , pp. 1021-1029
    • Camastra, F.1    Filippone, M.2
  • 7
    • 77955956946 scopus 로고    scopus 로고
    • Crystal fingerprint space - A novel paradigm for studying crystal-structure sets
    • M. Valle, and A.R. Oganov Crystal fingerprint space - a novel paradigm for studying crystal-structure sets Acta Crystallogr. Sect. A 66 5 2010 507 517
    • (2010) Acta Crystallogr. Sect. A , vol.66 , Issue.5 , pp. 507-517
    • Valle, M.1    Oganov, A.R.2
  • 12
    • 0142025120 scopus 로고    scopus 로고
    • Data dimensionality estimation methods: A survey
    • DOI 10.1016/S0031-3203(03)00176-6
    • F. Camastra Data dimensionality estimation methods a survey Pattern Recognit. 36 12 2003 2945 2954 (Pubitemid 37293405)
    • (2003) Pattern Recognition , vol.36 , Issue.12 , pp. 2945-2954
    • Camastra, F.1
  • 15
    • 0015011520 scopus 로고
    • An algorithm for finding intrinsic dimensionality of data
    • K. Fukunaga An algorithm for finding intrinsic dimensionality of data IEEE Trans. Comput. 20 1971 176 183
    • (1971) IEEE Trans. Comput. , vol.20 , pp. 176-183
    • Fukunaga, K.1
  • 17
    • 0038959172 scopus 로고    scopus 로고
    • Probabilistic principal component analysis
    • PART 3
    • M.E. Tipping, and C.M. Bishop Probabilistic principal component analysis J. R. Stat. Soc. Ser. B 61 Part 3 1997 611 622
    • (1997) J. R. Stat. Soc. Ser. B , vol.61 , pp. 611-622
    • Tipping, M.E.1    Bishop, C.M.2
  • 18
    • 0003285226 scopus 로고    scopus 로고
    • Bayesian PCA
    • C.M. Bishop, Bayesian PCA, in: Proceedings of NIPS, vol. 11, 1998, pp. 382-388.
    • (1998) Proceedings of NIPS , vol.11 , pp. 382-388
    • Bishop, C.M.1
  • 19
    • 84862276325 scopus 로고    scopus 로고
    • Simple exponential family PCA
    • J. Li, D. Tao, Simple exponential family PCA, in: Proceedings of AISTATS, 2010, pp. 453-460.
    • (2010) Proceedings of AISTATS , pp. 453-460
    • Li, J.1    Tao, D.2
  • 21
    • 84862296954 scopus 로고    scopus 로고
    • Sparse probabilistic principal component analysis
    • Y. Guan, and J.G. Dy Sparse probabilistic principal component analysis J. Mach. Learn. Res. - Proc. Track 5 2009 185 192
    • (2009) J. Mach. Learn. Res. - Proc. Track , vol.5 , pp. 185-192
    • Guan, Y.1    Dy, J.G.2
  • 22
    • 78649400333 scopus 로고    scopus 로고
    • Maximum likelihood estimation of intrinsic dimension
    • E. Levina, and P.J. Bickel Maximum likelihood estimation of intrinsic dimension Proc. NIPS 171 2005 777 784
    • (2005) Proc. NIPS , vol.171 , pp. 777-784
    • Levina, E.1    Bickel, P.J.2
  • 24
    • 76749147912 scopus 로고    scopus 로고
    • Dimensionality estimation, manifold learning and function approximation using tensor voting
    • P. Mordohai, and G. Medioni Dimensionality estimation, manifold learning and function approximation using tensor voting J. Mach. Learn. Res. 11 2010 411 450
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 411-450
    • Mordohai, P.1    Medioni, G.2
  • 29
    • 85156237082 scopus 로고    scopus 로고
    • Intrinsic dimension estimation using packing numbers
    • S. Becker, S. Thrun, K. Obermayer, MIT Press, Cambridge
    • B. Kégl Intrinsic dimension estimation using packing numbers S. Becker, S. Thrun, K. Obermayer, Proceedings of NIPS 2002 MIT Press, Cambridge 681 688
    • (2002) Proceedings of NIPS , pp. 681-688
    • Kégl, B.1
  • 30
    • 72349089295 scopus 로고    scopus 로고
    • Estimation of intrinsic dimensionality using high-rate vector quantization
    • M. Raginsky, S. Lazebnik, Estimation of intrinsic dimensionality using high-rate vector quantization, in: NIPS, 2005, pp. 1105-1112.
    • (2005) NIPS , pp. 1105-1112
    • Raginsky, M.1    Lazebnik, S.2
  • 32
    • 84885473999 scopus 로고    scopus 로고
    • Learning intrinsic dimension and entropy of high-dimensional shape spaces
    • J.A. Costa, A.O. Hero, Learning intrinsic dimension and entropy of high-dimensional shape spaces, in: Proceedings of EUSIPCO, 2004, pp. 1-22.
    • (2004) Proceedings of EUSIPCO , pp. 1-22
    • Costa, J.A.1    Hero, A.O.2
  • 33
    • 3543131272 scopus 로고    scopus 로고
    • Geodesic entropic graphs for dimension and entropy estimation in manifold learning
    • J.A. Costa, and A.O. Hero Geodesic entropic graphs for dimension and entropy estimation in manifold learning IEEE Trans. Signal Process. 52 8 2004 2210 2221
    • (2004) IEEE Trans. Signal Process. , vol.52 , Issue.8 , pp. 2210-2221
    • Costa, J.A.1    Hero, A.O.2
  • 34
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • DOI 10.1126/science.290.5500.2319
    • J. Tenenbaum, V. Silva, and J. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 2000 2319 2323 (Pubitemid 32041577)
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 35
    • 44049117207 scopus 로고
    • Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems
    • J.P. Eckmann, and D. Ruelle Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems Phys. D: Nonlinear Phenom. 56 2-3 1992 185 187
    • (1992) Phys. D: Nonlinear Phenom. , vol.56 , Issue.23 , pp. 185-187
    • Eckmann, J.P.1    Ruelle, D.2
  • 36
    • 39049106144 scopus 로고    scopus 로고
    • A nearest-neighbor approach to estimating divergence between continuous random vectors
    • DOI 10.1109/ISIT.2006.261842, 4035959, Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
    • Q. Wang, S.R. Kulkarni, S. Verdu, A nearest-neighbor approach to estimating divergence between continuous random vector, in: Proceedings of ISIT, 2006, pp. 242-246. (Pubitemid 351244090)
    • (2006) IEEE International Symposium on Information Theory - Proceedings , pp. 242-246
    • Wang, Q.1    Kulkarni, S.R.2    Verdu, S.3
  • 38
    • 82255164483 scopus 로고    scopus 로고
    • On the distribution of angles between the N shortest vectors in a random lattice
    • A. Sodergren On the distribution of angles between the N shortest vectors in a random lattice J. Lond. Math. Soc. 84 3 2011 749 764
    • (2011) J. Lond. Math. Soc. , vol.84 , Issue.3 , pp. 749-764
    • Sodergren, A.1
  • 40
    • 11744349798 scopus 로고
    • Analogues of the normal distribution on the circle and the sphere
    • E. Breitenberger Analogues of the normal distribution on the circle and the sphere Biometrika 50 1-2 1963 81 88
    • (1963) Biometrika , vol.50 , Issue.12 , pp. 81-88
    • Breitenberger, E.1
  • 41
    • 85041975153 scopus 로고
    • New approximations to the distribution of certain angular statistics
    • G.J.G. Upton New approximations to the distribution of certain angular statistics Biometrika 61 2 1974 369 373
    • (1974) Biometrika , vol.61 , Issue.2 , pp. 369-373
    • Upton, G.J.G.1
  • 43
    • 0017106789 scopus 로고
    • New approximations to the von Mises distribution
    • G.W. Hill New approximations to the von Mises distribution Biometrika 63 3 1976 673 676
    • (1976) Biometrika , vol.63 , Issue.3 , pp. 673-676
    • Hill, G.W.1
  • 44
    • 0037842762 scopus 로고
    • Approximate confidence intervals for the mean direction of a von Mises distribution
    • G.J.G. Upton Approximate confidence intervals for the mean direction of a von Mises distribution Biometrika 73 2 1986 525 527
    • (1986) Biometrika , vol.73 , Issue.2 , pp. 525-527
    • Upton, G.J.G.1
  • 46
    • 51749116500 scopus 로고    scopus 로고
    • Statistical image modeling using von Mises distribution in the complex directional wavelet domain
    • A.P.N. Vo, S. Oraintara, T.T. Nguyen, Statistical image modeling using von Mises distribution in the complex directional wavelet domain, in: Proceedings of ISCAS 2008, 2008, pp. 2885-2888.
    • (2008) Proceedings of ISCAS 2008 , pp. 2885-2888
    • Vo, A.P.N.1    Oraintara, S.2    Nguyen, T.T.3
  • 48
    • 0002834599 scopus 로고
    • The use of the Hankel transform in statistics I. General theory and examples
    • R.D. Lord The use of the Hankel transform in statistics I. General theory and examples Biometrika 41 1/2 1954 44 55
    • (1954) Biometrika , vol.41 , Issue.1-2 , pp. 44-55
    • Lord, R.D.1
  • 49
    • 0030303844 scopus 로고    scopus 로고
    • An interior, trust region approach for nonlinear minimization subject to bounds
    • T.F. Coleman, and Y. Li An interior, trust region approach for nonlinear minimization subject to bounds SIAM J. Optim. 6 1996 418 445
    • (1996) SIAM J. Optim. , vol.6 , pp. 418-445
    • Coleman, T.F.1    Li, Y.2
  • 51
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proc. IEEE 86 1998 2278 2324
    • (1998) Proc. IEEE , vol.86 , pp. 2278-2324
    • Lecun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 58
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30 (Pubitemid 43022939)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demsar, J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.