메뉴 건너뛰기




Volumn 33, Issue 5, 2012, Pages 524-529

Model selection for partial least squares based dimension reduction

Author keywords

Dimension reduction; Model selection; Partial least squares

Indexed keywords

COMPUTATION LOADS; CROSS-VALIDATION METHODS; DATA SETS; DIMENSION REDUCTION; EFFECTIVE DIMENSIONS; MODEL SELECTION; NOVEL ALGORITHM; PARTIAL LEAST SQUARES; PRINCIPAL COMPONENTS; REAL WORLD DATA; SCIENTIFIC DATA;

EID: 84862821303     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.11.009     Document Type: Article
Times cited : (9)

References (24)
  • 2
    • 53649089323 scopus 로고    scopus 로고
    • Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques
    • D. Ballabio, T. Skov, R. Leard, and R. Bro Classification of GC-MS measurements of wines by combining data dimension reduction and variable selection techniques J. Chemom. 22 2008 457 463
    • (2008) J. Chemom. , vol.22 , pp. 457-463
    • Ballabio, D.1    Skov, T.2    Leard, R.3    Bro, R.4
  • 3
    • 0037350844 scopus 로고    scopus 로고
    • Partial least squares for discrimination
    • M. Barker, and W. Rayens Partial least squares for discrimination J. Chemometr. 17 2003 166 173
    • (2003) J. Chemometr. , vol.17 , pp. 166-173
    • Barker, M.1    Rayens, W.2
  • 4
    • 84958734809 scopus 로고
    • Further aspects of the theory of multiple regressions
    • M.S. Bartlett Further aspects of the theory of multiple regressions Proc. Camb. Philos. Soc. 34 1938 33 40
    • (1938) Proc. Camb. Philos. Soc. , vol.34 , pp. 33-40
    • Bartlett, M.S.1
  • 5
    • 44649110372 scopus 로고    scopus 로고
    • Partial least squares regression: A valuable method for modeling molecular behavior in hemodialysis
    • E.A. Fernandez, R. Valtuille, and P. Willshaw Partial least squares regression: A valuable method for modeling molecular behavior in hemodialysis Ann. Biomed. Eng. 36 2008 1305 1313
    • (2008) Ann. Biomed. Eng. , vol.36 , pp. 1305-1313
    • Fernandez, E.A.1    Valtuille, R.2    Willshaw, P.3
  • 6
    • 60149097675 scopus 로고    scopus 로고
    • A robust partial least squares regression method with applications
    • J. Gonzalez, D. Pena, and R. Romera A robust partial least squares regression method with applications J. Chemometr. 23 2009 78 90
    • (2009) J. Chemometr. , vol.23 , pp. 78-90
    • Gonzalez, J.1    Pena, D.2    Romera, R.3
  • 7
    • 0037191154 scopus 로고    scopus 로고
    • Model selection for partial least squares regression
    • DOI 10.1016/S0169-7439(02)00051-5, PII S0169743902000515
    • B. Li, J. Morris, and E.B. Martin Model selection for partial least squares regression Chemom. Intell. Lab. Syst. 64 2002 79 89 (Pubitemid 35304561)
    • (2002) Chemometrics and Intelligent Laboratory Systems , vol.64 , Issue.1 , pp. 79-89
    • Li, B.1    Morris, J.2    Martin, E.B.3
  • 8
    • 44949177349 scopus 로고    scopus 로고
    • Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis
    • G.-Z. Li, H.-L. Bu, M.Q. Yang, X.-Q. Zeng, and J.Y. Yang Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis BMC Genomics 9 S2 2008 S24
    • (2008) BMC Genomics , vol.9 , Issue.S2 , pp. 24
    • Li, G.-Z.1    Bu, H.-L.2    Yang, M.Q.3    Zeng, X.-Q.4    Yang, J.Y.5
  • 9
    • 67949091312 scopus 로고    scopus 로고
    • Chapter 1, Feature selection for partial least square based dimension reduction
    • Springer
    • G.-Z. Li, and X.-Q. Zeng Chapter 1, Feature selection for partial least square based dimension reduction Studies in Computational Intelligence vol. 205 2009 Springer 3 37
    • (2009) Studies in Computational Intelligence , vol.205 , pp. 3-37
    • Li, G.-Z.1    Zeng, X.-Q.2
  • 10
    • 67650214895 scopus 로고    scopus 로고
    • A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling
    • Z.C. Liu, M. Xiang, Y.D. Wen, Y. Wang, W.S. Cai, and X.G. Shao A practical approach for near infrared spectral quantitative analysis of complex samples using partial least squares modeling Sci. China Ser. B-Chem. 52 2009 1021 1027
    • (2009) Sci. China Ser. B-Chem. , vol.52 , pp. 1021-1027
    • Liu, Z.C.1    Xiang, M.2    Wen, Y.D.3    Wang, Y.4    Cai, W.S.5    Shao, X.G.6
  • 11
    • 20444468926 scopus 로고    scopus 로고
    • Genome-based identification of diagnostic molecular markers for human lung carcinomas by PLS-DA
    • DOI 10.1016/j.compbiolchem.2005.04.005, PII S1476927105000368
    • G. Musumarra, V. Barresi, and D.F. Condorelli Genome-based identification of diagnostic molecular markers for human lung carcinomas by PLS-DA Comput. Biol. Chem. 29 2005 183 195 (Pubitemid 40822858)
    • (2005) Computational Biology and Chemistry , vol.29 , Issue.3 , pp. 183-195
    • Musumarra, G.1    Barresi, V.2    Condorelli, D.F.3    Fortuna, C.G.4    Scire, S.5
  • 12
    • 0036166439 scopus 로고    scopus 로고
    • Tumor classification by partial least squares using microarray gene expression data
    • D.V. Nguyen, and D.M. Rocke Tumor classification by partial least squares using microarray gene expression data Bioinformatics 18 2002 39 50 (Pubitemid 34145030)
    • (2002) Bioinformatics , vol.18 , Issue.1 , pp. 39-50
    • Nguyen, D.V.1    Rocke, D.M.2
  • 13
    • 0036740519 scopus 로고    scopus 로고
    • Multi-class cancer classification via partial least squares with gene expression profiles
    • D.V. Nguyen, and D.M. Rocke Multi-class cancer classification via partial least squares with gene expression profiles Bioinformatics 18 2002 1216 1226
    • (2002) Bioinformatics , vol.18 , pp. 1216-1226
    • Nguyen, D.V.1    Rocke, D.M.2
  • 14
    • 19044395321 scopus 로고    scopus 로고
    • How many principal components? stopping rules for determining the number of non-trivial axes revisited
    • DOI 10.1016/j.csda.2004.06.015, PII S0167947304002014
    • R. Peres-Neto Pedro, D.A. Jackson, and K.M. Somers How many principal components? Stopping rules for determining the number of non-trivial axes revisited Comput. Stat. Data Anal. 49 2005 974 997 (Pubitemid 40710156)
    • (2005) Computational Statistics and Data Analysis , vol.49 , Issue.4 , pp. 974-997
    • Peres-Neto, P.R.1    Jackson, D.A.2    Somers, K.M.3
  • 15
    • 77953688323 scopus 로고    scopus 로고
    • An asymmetric classifier based on partial least squares
    • H.-N. Qu, G.-Z. Li, and W.-S. Xu An asymmetric classifier based on partial least squares Pattern Recognit. (Elsevier) 43 2010 3448 3457
    • (2010) Pattern Recognit. (Elsevier) , vol.43 , pp. 3448-3457
    • Qu, H.-N.1    Li, G.-Z.2    Xu, W.-S.3
  • 18
    • 0035914529 scopus 로고    scopus 로고
    • PoLiSh-smoothed partial least-squares regression
    • D.N. Rutledge, A. Barros, and I. Delgadillo PoLiSh-smoothed partial least-squares regression Anal. Chim. Acta 446 2001 281 296
    • (2001) Anal. Chim. Acta , vol.446 , pp. 281-296
    • Rutledge, D.N.1    Barros, A.2    Delgadillo, I.3
  • 19
    • 70349202016 scopus 로고    scopus 로고
    • Classification for high-throughput data with an optimal subset of principal components
    • J.J. Song, Y. Ren, and F. Yan Classification for high-throughput data with an optimal subset of principal components Comput. Biol. Chem. 33 2009 408 413
    • (2009) Comput. Biol. Chem. , vol.33 , pp. 408-413
    • Song, J.J.1    Ren, Y.2    Yan, F.3
  • 20
    • 34547691339 scopus 로고    scopus 로고
    • Extracting gene regulation information for cancer classification
    • H.Q. Wang, H.S. Wong, and D.S. Huang Extracting gene regulation information for cancer classification Pattern Recogn. 40 2007 3379 3392
    • (2007) Pattern Recogn. , vol.40 , pp. 3379-3392
    • Wang, H.Q.1    Wong, H.S.2    Huang, D.S.3
  • 22
    • 33746677949 scopus 로고    scopus 로고
    • A stable gene selection in microarray data analysis
    • K. Yang, Z. Cai, J. Li, and G. Lin A stable gene selection in microarray data analysis BMC Bioinf. 7 2006 228
    • (2006) BMC Bioinf. , vol.7 , pp. 228
    • Yang, K.1    Cai, Z.2    Li, J.3    Lin, G.4
  • 23
    • 63049083722 scopus 로고    scopus 로고
    • Irrelevant gene elimination for partial least squares based dimension reduction by using feature probes
    • X.-Q. Zeng, G.-Z. Li, G.-F. Wu, J.Y. Yang, and M.Q. Yang Irrelevant gene elimination for partial least squares based dimension reduction by using feature probes Int. J. Data Mining Bioinform. Indersci. 3 1 2009 85 103
    • (2009) Int. J. Data Mining Bioinform. Indersci. , vol.3 , Issue.1 , pp. 85-103
    • Zeng, X.-Q.1    Li, G.-Z.2    Wu, G.-F.3    Yang, J.Y.4    Yang, M.Q.5
  • 24
    • 33846344646 scopus 로고    scopus 로고
    • Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies
    • Y.P. Zhou, C.B. Cai, H. Shi, J.H. Jiang, H.L. Wu, G.L. Shen, and R.Q. Yu Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies Talanta 71 2007 848 853
    • (2007) Talanta , vol.71 , pp. 848-853
    • Zhou, Y.P.1    Cai, C.B.2    Shi, H.3    Jiang, J.H.4    Wu, H.L.5    Shen, G.L.6    Yu, R.Q.7


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.