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Volumn 44, Issue 7, 2011, Pages 1357-1371

Supervised principal component analysis: Visualization, classification and regression on subspaces and submanifolds

Author keywords

Classification; Dimensionality reduction; Kernel methods; Principal component analysis (PCA); Regression; Supervised learning; Visualization

Indexed keywords

CLASSIFICATION; DIMENSIONALITY REDUCTION; KERNEL METHODS; PRINCIPAL COMPONENT ANALYSIS (PCA); REGRESSION;

EID: 79952190499     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2010.12.015     Document Type: Article
Times cited : (293)

References (56)
  • 3
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • R.A. Fisher The use of multiple measurements in taxonomic problems Annals of Eugenics 7 1936 179 188
    • (1936) Annals of Eugenics , vol.7 , pp. 179-188
    • Fisher, R.A.1
  • 5
    • 21844452080 scopus 로고    scopus 로고
    • Integrating constraints and metric learning in semi-supervised clustering
    • M. Bilenko, S. Basu, R.J. Mooney, Integrating constraints and metric learning in semi-supervised clustering, in: ICML, vol. 69, 2004, p. 11.
    • (2004) ICML , vol.69 , pp. 11
    • Bilenko, M.1    Basu, S.2    Mooney, R.J.3
  • 7
    • 33646084850 scopus 로고    scopus 로고
    • Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
    • H. Chang, and D.-Y. Yeung Locally linear metric adaptation with application to semi-supervised clustering and image retrieval Pattern Recognition 39 7 2006 1253 1264
    • (2006) Pattern Recognition , vol.39 , Issue.7 , pp. 1253-1264
    • Chang, H.1    Yeung, D.-Y.2
  • 8
    • 33244489358 scopus 로고    scopus 로고
    • Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints
    • DOI 10.1016/j.patcog.2005.12.004, PII S0031320305004577
    • D.-Y. Yeung, and H. Chang Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints Pattern Recognition 39 5 2006 1007 1010 (Pubitemid 43276113)
    • (2006) Pattern Recognition , vol.39 , Issue.5 , pp. 1007-1010
    • Yeung, D.-Y.1    Chang, H.2
  • 13
    • 84945116550 scopus 로고
    • Sliced inverse regression for dimension reduction (with discussion)
    • K. Li Sliced inverse regression for dimension reduction (with discussion) Journal of the American Statistical Association 86 1991 316 342
    • (1991) Journal of the American Statistical Association , vol.86 , pp. 316-342
    • Li, K.1
  • 15
    • 84950441056 scopus 로고
    • On principal hessian directions for data visualization and dimension reduction: Another application of Stein's lemma
    • K. Li On principal hessian directions for data visualization and dimension reduction: another application of Stein's lemma Journal of the American Statistical Association 87 1992 1025 1039
    • (1992) Journal of the American Statistical Association , vol.87 , pp. 1025-1039
    • Li, K.1
  • 16
    • 21144467148 scopus 로고
    • Exploring regression structure using nonparametric functional estimation
    • A.M. Samarov Exploring regression structure using nonparametric functional estimation Journal of the American Statistical Association 88 1993 836 847
    • (1993) Journal of the American Statistical Association , vol.88 , pp. 836-847
    • Samarov, A.M.1
  • 17
    • 0012657603 scopus 로고    scopus 로고
    • Dimension reduction and visualization in discriminant analysis (with discussion)
    • R.D. Cook, and X. Yin Dimension reduction and visualization in discriminant analysis (with discussion) Australian & New-Zealand Journal of Statistics 43 2001 147 199 (Pubitemid 33613436)
    • (2001) Australian and New Zealand Journal of Statistics , vol.43 , Issue.2 , pp. 147-199
    • Cook, R.D.1    Yin, X.2
  • 18
    • 0035528162 scopus 로고    scopus 로고
    • Structure adaptive approach for dimension reduction
    • M. Hristache, A. Juditsky, J. Polzehl, and V. Spokoiny Structure adaptive approach for dimension reduction The Annals of Statistics 29 2001 1537 1566 (Pubitemid 33405985)
    • (2001) Annals of Statistics , vol.29 , Issue.6 , pp. 1537-1566
    • Hristache, M.1    Juditsky, A.2    Polzehl, J.3    Spokoiny, V.4
  • 19
    • 1942450610 scopus 로고    scopus 로고
    • Feature extraction by non-parametric mutual information maximization
    • K. Torkkola Feature extraction by non-parametric mutual information maximization Journal of Machine Learning Research 3 2003 1415 1438
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1415-1438
    • Torkkola, K.1
  • 20
    • 4544371135 scopus 로고    scopus 로고
    • Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces
    • K. Fukumizu, F.R. Bach, and M.I. Jordan Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces Journal of Machine Learning Research 5 2004 73 99
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 73-99
    • Fukumizu, K.1    Bach, F.R.2    Jordan, M.I.3
  • 23
    • 34547968745 scopus 로고    scopus 로고
    • Regression on manifolds using kernel dimension reduction
    • J. Nilsson, F. Sha, M.I. Jordan, Regression on manifolds using kernel dimension reduction, in: ICML, vol. 227, 2007, pp. 697704.
    • (2007) ICML , vol.227 , pp. 697-704
    • Nilsson, J.1    Sha, F.2    Jordan, M.I.3
  • 24
    • 33646528415 scopus 로고    scopus 로고
    • Measuring statistical dependence with Hilbert-Schmidt norms
    • A. Gretton, O. Bousquet, A.J. Smola, B. Schlkopf, Measuring statistical dependence with HilbertSchmidt norms, in: Proceedings Algorithmic Learning Theory (ALT), vol. 3734, 2005, pp. 6377.
    • (2005) Proceedings Algorithmic Learning Theory (ALT) , vol.3734 , pp. 63-77
    • Gretton, A.1
  • 25
    • 34547964410 scopus 로고    scopus 로고
    • Supervised feature selection via dependence estimation
    • L. Song, A.J. Smola, A. Gretton, K.M. Borgwardt, J. Bedo, Supervised feature selection via dependence estimation, in: ICML, vol. 227, 2007, pp. 823830.
    • (2007) ICML , vol.227 , pp. 823-830
    • Song, L.1    Smola, A.J.2    Gretton, A.3    Borgwardt, K.M.4    Bedo, J.5
  • 27
    • 34547972314 scopus 로고    scopus 로고
    • A dependence maximization view of clustering
    • L. Song, A.J. Smola, A. Gretton, K.M. Borgwardt, A dependence maximization view of clustering, in: ICML, vol. 227, 2007, pp. 815822.
    • (2007) ICML , vol.227 , pp. 815-822
    • Song, L.1    Smola, A.J.2    Gretton, A.3    Borgwardt, K.M.4
  • 36
    • 0000957593 scopus 로고
    • Principal components regression in exploratory statistical research
    • W.F. Massy Principal components regression in exploratory statistical research Journal of the American Statistical Association 60 1965 234 256
    • (1965) Journal of the American Statistical Association , vol.60 , pp. 234-256
    • Massy, W.F.1
  • 37
    • 26444566340 scopus 로고    scopus 로고
    • Contour regression: A general approach to dimension reduction
    • DOI 10.1214/009053605000000192
    • B. Li, H. Zha, F. Chiaromonte, Contour regression: a general approach to dimension reduction, in: ICML, vol. 33, 2005, pp. 15801616. (Pubitemid 41423981)
    • (2005) Annals of Statistics , vol.33 , Issue.4 , pp. 1580-1616
    • Li, B.1    Zha, H.2    Chiaromonte, F.3
  • 41
    • 0012184060 scopus 로고    scopus 로고
    • A survey of partial least squares (pls) methods, with emphasis on the two-block case
    • University of Washington
    • J.A. Wegelin, A survey of partial least squares (pls) methods, with emphasis on the two-block case, Technical Report, University of Washington, 2000.
    • (2000) Technical Report
    • Wegelin, J.A.1
  • 42
    • 0038259120 scopus 로고    scopus 로고
    • Kernel partial least squares regression in reproducing kernel Hilbert space
    • R. Rosipal, and L.J. Trejo Kernel partial least squares regression in reproducing kernel Hilbert space Journal of Machine Learning Research 2 2001 97 123
    • (2001) Journal of Machine Learning Research , vol.2 , pp. 97-123
    • Rosipal, R.1    Trejo, L.J.2
  • 46
    • 84864063320 scopus 로고    scopus 로고
    • Sparse kernel orthonormalized pls for feature extraction in large data sets
    • J. Arenas-Garca, K.B. Petersen, L.K. Hansen, Sparse kernel orthonormalized pls for feature extraction in large data sets, in: Advances in Neural Information Processing Systems, 2007, p. 33.
    • (2007) Advances in Neural Information Processing Systems , pp. 33
    • Arenas-García, J.1
  • 47
    • 0037624001 scopus 로고    scopus 로고
    • Kernel partial least squares for nonlinear regression and discrimination
    • R. Rosipal Kernel partial least squares for nonlinear regression and discrimination Neural Network World 13 3 2003 291 300
    • (2003) Neural Network World , vol.13 , Issue.3 , pp. 291-300
    • Rosipal, R.1
  • 48
    • 0000107975 scopus 로고
    • Relations between two sets of variables
    • H. Hotelling Relations between two sets of variables Biometrika 28 1936 312 377
    • (1936) Biometrika , vol.28 , pp. 312-377
    • Hotelling, H.1
  • 49
    • 10044285992 scopus 로고    scopus 로고
    • Canonical correlation analysis: An overview with application to learning methods
    • DOI 10.1162/0899766042321814
    • D. Hardoon, S. Szedmak, and J. Shawe-taylor Canonical correlation analysis: an overview with application to learning methods Neural Computation 16 12 2004 2639 2664 (Pubitemid 39604012)
    • (2004) Neural Computation , vol.16 , Issue.12 , pp. 2639-2664
    • Hardoon, D.R.1    Szedmak, S.2    Shawe-Taylor, J.3
  • 53
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • DOI 10.1126/science.290.5500.2323
    • S. Roweis, and L. Saul Nonlinear dimensionality reduction by locally linear embedding Science 290 2000 2323 2326 (Pubitemid 32041578)
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 54
    • 84880203756 scopus 로고    scopus 로고
    • Laplacian eigenmaps and spectral techniques for embedding and clustering
    • M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Advances in Neural Information Processing Systems, vol. 1, 2002, pp. 585592.
    • (2002) Advances in Neural Information Processing Systems , vol.1 , pp. 585-592
    • Belkin, M.1    Niyogi, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.