메뉴 건너뛰기




Volumn , Issue , 2009, Pages 3-22

A unification of component analysis methods

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DISCRIMINANT ANALYSIS; LEAST SQUARES APPROXIMATIONS; PROBLEM SOLVING;

EID: 84969718516     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/9789814273398_001     Document Type: Chapter
Times cited : (7)

References (80)
  • 2
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • B. Sehölkopf, A. Smola, and K. Muller, Nonlinear component analysis as a kernel eigenvalue problem., Neural Computation. 10, 1299-1319, (1998).
    • (1998) Neural Computation , vol.10 , pp. 1299-1319
    • Sehölkopf, B.1    Smola, A.2    Muller, K.3
  • 3
    • 0001474381 scopus 로고
    • The statistieal utilization of multiple measurements
    • R. A. Fisher, The statistieal utilization of multiple measurements. Armais of Eugénies. 8, 376-386, (1938).
    • (1938) Armais of Eugénies , vol.8 , pp. 376-386
    • Fisher, R.A.1
  • 4
    • 0000107975 scopus 로고
    • Relations between two sets of variâtes
    • H. Hotellmg, Relations between two sets of variâtes, Biometrika. 28, 321-377, (1936).
    • (1936) Biometrika , vol.28 , pp. 321-377
    • Hotellmg, H.1
  • 8
    • 0033556862 scopus 로고    scopus 로고
    • A unifying review of linear gaussian models
    • S. Roweis and Z. Ghahramani, A unifying review of linear gaussian models.. Neural Computa-tion. 11(2), 305-345, (1999).
    • (1999) Neural Computa-Tion , vol.11 , Issue.2 , pp. 305-345
    • Roweis, S.1    Ghahramani, Z.2
  • 9
    • 33947194180 scopus 로고    scopus 로고
    • Graph embedding: A general framework for dimen-sionality réduction
    • S. Yan, D. Xu, B. Zhang, and H. Zhang, Graph embedding: A general framework for dimen-sionality réduction, PAMI. 29(1), 40-51, (2007).
    • (2007) PAMI , vol.29 , Issue.1 , pp. 40-51
    • Yan, S.1    Xu, D.2    Zhang, B.3    Zhang, H.4
  • 12
    • 0001497505 scopus 로고
    • Estimating linear restrictions on régression coefficients for multivariate normal distnbutions
    • T. W. Anderson, Estimating linear restrictions on régression coefficients for multivariate normal distnbutions, Ann. Math. Statist. 12, 327-351, (1951).
    • (1951) Ann. Math. Statist , vol.12 , pp. 327-351
    • Anderson, T.W.1
  • 20
    • 0024774330 scopus 로고
    • Neural networks and principal component analysis: Leaming from examples without local minima
    • P. Baldi and K. Homik, Neural networks and principal component analysis: Leaming from examples without local minima, Neural Networks. 2, 53-58, (1989).
    • (1989) Neural Networks , vol.2 , pp. 53-58
    • Baldi, P.1    Homik, K.2
  • 21
    • 0029220876 scopus 로고
    • Visual leaming and récognition of 3D objects from appearance
    • H. Murase and S. K. Nayar, Visual leaming and récognition of 3D objects from appearance. International Journal of Computer vision. 1(14), 5-24, (1995).
    • (1995) International Journal of Computer Vision , vol.1 , Issue.14 , pp. 5-24
    • Murase, H.1    Nayar, S.K.2
  • 23
    • 0004236492 scopus 로고
    • 2nd ed. The Johns Hopkins University Press
    • G. Golub and C. F. V. Loan. Matrix Computations. (2nd ed. The Johns Hopkins University Press, 1989).
    • (1989) Matrix Computations.
    • Golub, G.1    Loan, C.F.2
  • 29
    • 0001710505 scopus 로고
    • Analysis of a complex of statistieal variables into principal components
    • H. Hotelling, Analysis of a complex of statistieal variables into principal components, Journal of Educational Psychology. 24, (1933).
    • (1933) Journal of Educational Psychology
    • Hotelling, H.1
  • 30
    • 0020464111 scopus 로고
    • A simplified neuron model as principal component analyzer
    • E. Oja, A simplified neuron model as principal component analyzer, Journal of Mathematical Biology. 15, 267-273, (1982).
    • (1982) Journal of Mathematical Biology , vol.15 , pp. 267-273
    • Oja, E.1
  • 31
    • 84898929664 scopus 로고    scopus 로고
    • EM algorithms for PC'A and SPC'A
    • S. Roweis. EM algorithms for PC'A and SPC'A. In NWS, pp. 626-632, (1997).
    • (1997) NWS , pp. 626-632
    • Roweis, S.1
  • 32
    • 0018546040 scopus 로고
    • Lower rank approximation of matrices by least squares with any choice of weights
    • K. R. Gabriel and S. Zamir, Lower rank approximation of matrices by least squares with any choice of weights, Technometrics, Vol. 21, pp. 21, 489-498, (1979).
    • (1979) Technometrics , vol.21
    • Gabriel, K.R.1    Zamir, S.2
  • 33
    • 0029373923 scopus 로고
    • Principal component analysis with missing data and its application to polyhedral object modeling
    • H. Shum, K. Ikeuchi, and R. Reddv. Principal component analysis with missing data and its application to polyhedral object modeling, Pattem Analysis and Machine Intelligence. 17(9), 855-867, (1995).
    • (1995) Pattem Analysis and Machine Intelligence , vol.17 , Issue.9 , pp. 855-867
    • Shum, H.1    Ikeuchi, K.2    Reddv, R.3
  • 40
    • 31644433509 scopus 로고    scopus 로고
    • Combming reconstmetive and discriminative subspace methods for robust classification and régression by subsampling
    • S. Fidler, D. Skocaj, and A. Leonardis, Combming reconstmetive and discriminative subspace methods for robust classification and régression by subsampling, IEEE Transactions on Pattern Analysis and Machine Litelligence. 28(3), 337-350, (2006).
    • (2006) IEEE Transactions on Pattern Analysis and Machine Litelligence , vol.28 , Issue.3 , pp. 337-350
    • Fidler, S.1    Skocaj, D.2    Leonardis, A.3
  • 45
    • 34147152930 scopus 로고    scopus 로고
    • Discriminant component analysis for face récognition
    • W. Zhao. Discriminant component analysis for face récognition. In ICPR, pp. 818-821, (2000).
    • (2000) ICPR , pp. 818-821
    • Zhao, W.1
  • 47
    • 21844447839 scopus 로고    scopus 로고
    • Characterization of a family of algorithms for generalized discriminant analysis on un-dersampled problems
    • September
    • J. Ye, Characterization of a family of algorithms for generalized discriminant analysis on un-dersampled problems, The Journal of Machine Learning Research. 6(1), 483-502 (September, 2005).
    • (2005) The Journal of Machine Learning Research , vol.6 , Issue.1 , pp. 483-502
    • Ye, J.1
  • 48
    • 36048970830 scopus 로고    scopus 로고
    • Discriminant subspace analysis: A fukunaga-koontz approach
    • S. Zhang and T. Sim. Discriminant subspace analysis: A fukunaga-koontz approach., PAMÍ 29, 1732-1745, (2007).
    • (2007) PAMÍ , vol.29 , pp. 1732-1745
    • Zhang, S.1    Sim, T.2
  • 50
    • 0025746876 scopus 로고
    • On the relations between discriminant analysis and multilayer perceptrons
    • P. Gallinari, S. Thiria, F. Badran, and F. Fogelman-Soulie, On the relations between discriminant analysis and multilayer perceptrons. Neural Networks. 4, 349-360, (1991).
    • (1991) Neural Networks , vol.4 , pp. 349-360
    • Gallinari, P.1    Thiria, S.2    Badran, F.3    Fogelman-Soulie, F.4
  • 51
    • 34547964600 scopus 로고    scopus 로고
    • Least squares linear discriminant analysis
    • J. Ye. Least squares linear discriminant analysis. In ICML, pp. 1087-1093, (2007).
    • (2007) ICML , pp. 1087-1093
    • Ye, J.1
  • 52
    • 33749243962 scopus 로고    scopus 로고
    • Discriminative cluster analysis
    • New York, NY, USAJune, ACM Press
    • F. de la Torre and T. Kanade. Discriminative cluster analysis. In Liternational Conference on Machine Learning; vol. 148, pp. 241-248, New York, NY, USA (June, 2006). ACM Press.
    • (2006) Liternational Conference on Machine Learnin , vol.148 , pp. 241-248
    • De La Torre, F.1    Kanade, T.2
  • 54
    • 34247568488 scopus 로고    scopus 로고
    • Discriminative leaming and récognition of image set classes using canonical corrélations
    • T.-K. Kim, J. Kittler, and R. Cipolla., Discriminative leaming and récognition of image set classes using canonical corrélations, IEEE Trans, on PAMI. 29(6), 1005-1018, (2007).
    • (2007) Ieee Trans, on Pami. , vol.29 , Issue.6 , pp. 1005-1018
    • Kim, T.-K.1    Kittler, J.2    Cipolla, R.3
  • 56
    • 0038648412 scopus 로고    scopus 로고
    • Appearance models based on kernel canonical corrélation analysis
    • T. Melzer, M. Reiter, and H. Bischof, Appearance models based on kernel canonical corrélation analysis, Pattern Récognition. 36(9), 1961-1971, (2003).
    • (2003) Pattern Récognition , vol.36 , Issue.9 , pp. 1961-1971
    • Melzer, T.1    Reiter, M.2    Bischof, H.3
  • 58
    • 0030324059 scopus 로고    scopus 로고
    • Singular value décomposition analysis and canonical corrélation analysis
    • S. Cherry, Singular value décomposition analysis and canonical corrélation analysis., J. Clima te. (9), 2003-2009, (1997).
    • (1997) J. Clima Te , Issue.9 , pp. 2003-2009
    • Cherry, S.1
  • 59
    • 0343503881 scopus 로고
    • Canonical variables as optimal predictors
    • V. J. Yohai and M. S. Garcia, Canonical variables as optimal predictors., The Armais ofStatistics. 8(4), 865-869, (1980).
    • (1980) The Armais Ofstatistics , vol.8 , Issue.4 , pp. 865-869
    • Yohai, V.J.1    Garcia, M.S.2
  • 60
  • 65
    • 14344257496 scopus 로고    scopus 로고
    • K-means clustering via principal component analysis
    • C. Ding and X. He. K-means clustering via principal component analysis. In International Con-ference on Machine Learning, vol. 1, pp. 225-232, (2004).
    • (2004) International Con-Ference on Machine Learning , vol.1 , pp. 225-232
    • Ding, C.1    He, X.2
  • 70
    • 84969746202 scopus 로고    scopus 로고
    • (C'BMS Regional Conference Series in Mathemtics, vol, American Mathematical Society, Providence
    • F. K. Chung, Spectral Graph Theory. (C'BMS Regional Conference Series in Mathemtics, vol 92, American Mathematical Society, Providence, 1997).
    • (1997) Spectral Graph Theory , vol.92
    • Chung, F.K.1
  • 71
    • 0026925324 scopus 로고
    • New spectral methods for ratio eut partitioning and clustering
    • L. Hagen and A. Kahng, New spectral methods for ratio eut partitioning and clustering., IEEE. Trans, on Computed Aided Desgin. (11), 1074-1085, (1992).
    • (1992) Ieee. Trans, on Computed Aided Desgin , Issue.11 , pp. 1074-1085
    • Hagen, L.1    Kahng, A.2
  • 72
    • 84969670320 scopus 로고    scopus 로고
    • C'ompanson of spectral clustermg methods
    • D. Verma and M. Meila. C'ompanson of spectral clustermg methods. In NIPS, (2003).
    • (2003) NIPS
    • Verma, D.1    Meila, M.2
  • 74
  • 75
    • 84969682714 scopus 로고    scopus 로고
    • A imified view of kemel k-means, spectral clustering and graph partitioning
    • I. S. Dhilloil, Y. Guan, and B. Kulis. A imified view of kemel k-means, spectral clustering and graph partitioning. In UTCS Tech. Report TR-04-25, (2004).
    • (2004) UTCS Tech. Report TR-04-25
    • Dhilloil, I.S.1    Guan, Y.2    Kulis, B.3
  • 76
    • 0033285447 scopus 로고    scopus 로고
    • Segmentation using eigenvectors: A unifying view
    • Y. Weiss. Segmentation using eigenvectors: a unifying view. In ICCV, (1999).
    • (1999) ICCV
    • Weiss, Y.1
  • 77
    • 77952345425 scopus 로고    scopus 로고
    • Technical report, doctoral dissertation, tech. report CMU-RI-TR-06-44, Robotics Institute, Carnegie Mellon University (August,)
    • D. Tolliver. Spectral roimdmg and image segmentation. Technical report, doctoral dissertation, tech. report CMU-RI-TR-06-44, Robotics Institute, Carnegie Mellon University (August, 2006).
    • (2006) Spectral Roimdmg and Image Segmentation
    • Tolliver, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.