메뉴 건너뛰기




Volumn 53, Issue , 2016, Pages 73-86

Overfitting in linear feature extraction for classification of high-dimensional image data

Author keywords

Classification; Dimensionality reduction; Feature extraction; High dimensional datasets; Overfitting

Indexed keywords

EXTRACTION; FEATURE EXTRACTION; IMAGE CLASSIFICATION;

EID: 84958673886     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2015.11.015     Document Type: Article
Times cited : (96)

References (41)
  • 3
    • 63249112814 scopus 로고
    • Dimensionality and sample size considerations in pattern recognition practice
    • A. Jain Dimensionality and sample size considerations in pattern recognition practice Handbook of Statistics, Krishnaiah 2 1982 835 855
    • (1982) Handbook of Statistics, Krishnaiah , vol.2 , pp. 835-855
    • Jain, A.1
  • 4
    • 21744462998 scopus 로고    scopus 로고
    • On bias, variance, 0/1loss, and the curse-of-dimensionality
    • J.H. Friedman On bias, variance, 0/1loss, and the curse-of-dimensionality Data Min. Knowl. Discov. 1 1 1997 55 77
    • (1997) Data Min. Knowl. Discov. , vol.1 , Issue.1 , pp. 55-77
    • Friedman, J.H.1
  • 5
    • 0026120032 scopus 로고
    • Small sample size effects in statistical pattern recognition: Recommendations for practitioners
    • S.J. Raudys, and A.K. Jain Small sample size effects in statistical pattern recognition: recommendations for practitioners IEEE Trans. Pattern Anal. Mach. Intell. 13 3 1991 252 264
    • (1991) IEEE Trans. Pattern Anal. Mach. Intell. , vol.13 , Issue.3 , pp. 252-264
    • Raudys, S.J.1    Jain, A.K.2
  • 7
  • 8
    • 2442695245 scopus 로고    scopus 로고
    • What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models
    • M.A. Babyak What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models Psychosom. Med. 66 3 2004 411 421
    • (2004) Psychosom. Med. , vol.66 , Issue.3 , pp. 411-421
    • Babyak, M.A.1
  • 10
    • 34247558132 scopus 로고    scopus 로고
    • Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters
    • G.C. Cawley, and N.L. Talbot Preventing over-fitting during model selection via Bayesian regularisation of the hyper-parameters J. Mach. Learn. Res. 8 2007 841 861
    • (2007) J. Mach. Learn. Res. , vol.8 , pp. 841-861
    • Cawley, G.C.1    Talbot, N.L.2
  • 11
    • 77956907243 scopus 로고    scopus 로고
    • On over-fitting in model selection and subsequent selection bias in performance evaluation
    • G.C. Cawley, and N.L. Talbot On over-fitting in model selection and subsequent selection bias in performance evaluation J. Mach. Learn. Res. 11 2010 2079 2107
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 2079-2107
    • Cawley, G.C.1    Talbot, N.L.2
  • 13
    • 77952423823 scopus 로고    scopus 로고
    • Semi-supervised local fisher discriminant analysis for dimensionality reduction
    • M. Sugiyama, T. Idé, S. Nakajima, and J. Sese Semi-supervised local fisher discriminant analysis for dimensionality reduction Mach. Learn. 78 1-2 2010 35 61
    • (2010) Mach. Learn. , vol.78 , Issue.1-2 , pp. 35-61
    • Sugiyama, M.1    Idé, T.2    Nakajima, S.3    Sese, J.4
  • 14
    • 84878366601 scopus 로고    scopus 로고
    • An estimate of mutual information that permits closed-form optimisation
    • R. Liu, and D.F. Gillies An estimate of mutual information that permits closed-form optimisation Entropy 15 5 2013 1690 1704 10.3390/e15051690
    • (2013) Entropy , vol.15 , Issue.5 , pp. 1690-1704
    • Liu, R.1    Gillies, D.F.2
  • 16
    • 3242713335 scopus 로고    scopus 로고
    • Design of steerable filters for feature detection using canny-like criteria
    • M. Jacob, and M. Unser Design of steerable filters for feature detection using canny-like criteria IEEE Trans. Pattern Anal. Mach. Intell. 26 8 2004 1007 1019
    • (2004) IEEE Trans. Pattern Anal. Mach. Intell. , vol.26 , Issue.8 , pp. 1007-1019
    • Jacob, M.1    Unser, M.2
  • 18
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 19
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Y. Saeys, I. Inza, and P. Larrañaga A review of feature selection techniques in bioinformatics Bioinformatics 23 19 2007 2507 2517
    • (2007) Bioinformatics , vol.23 , Issue.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larrañaga, P.3
  • 21
    • 17644384367 scopus 로고    scopus 로고
    • Minimum redundancy feature selection from microarray gene expression data
    • C. Ding, and H. Peng Minimum redundancy feature selection from microarray gene expression data J. Bioinform. Comput. Biol. 3 02 2005 185 205
    • (2005) J. Bioinform. Comput. Biol. , vol.3 , Issue.2 , pp. 185-205
    • Ding, C.1    Peng, H.2
  • 22
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
    • H. Peng, F. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 1226 1238 (http://dx.doi.org/http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.159)
    • (2005) IEEE Trans. Pattern Anal. Mach. Intell. , vol.27 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 23
    • 2942723846 scopus 로고    scopus 로고
    • A divisive information theoretic feature clustering algorithm for text classification
    • I.S. Dhillon, S. Mallela, and R. Kumar A divisive information theoretic feature clustering algorithm for text classification J. Mach. Learn. Res. 3 2003 1265 1287
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1265-1287
    • Dhillon, I.S.1    Mallela, S.2    Kumar, R.3
  • 24
    • 2942731012 scopus 로고    scopus 로고
    • An extensive empirical study of feature selection metrics for text classification
    • G. Forman An extensive empirical study of feature selection metrics for text classification J. Mach. Learn. Res. 3 2003 1289 1305
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1289-1305
    • Forman, G.1
  • 25
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • J.B. Tenenbaum, V. De Silva, and J.C. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 5500 2000 2319 2323
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 26
    • 34548656483 scopus 로고    scopus 로고
    • Maximization of mutual information for supervised linear feature extraction
    • J. Leiva-Murillo, and A. Artes-Rodriguez Maximization of mutual information for supervised linear feature extraction IEEE Trans. Neural Netw. 18 5 2007 1433 1441
    • (2007) IEEE Trans. Neural Netw. , vol.18 , Issue.5 , pp. 1433-1441
    • Leiva-Murillo, J.1    Artes-Rodriguez, A.2
  • 27
    • 1942450610 scopus 로고    scopus 로고
    • Feature extraction by non parametric mutual information maximization
    • K. Torkkola Feature extraction by non parametric mutual information maximization J. Mach. Learn. Res. 3 2003 1415 1438
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1415-1438
    • Torkkola, K.1
  • 29
    • 0036487285 scopus 로고    scopus 로고
    • Why can LDA be performed in PCA transformed space?
    • J. Yang, and J.-y. Yang Why can LDA be performed in PCA transformed space? Pattern Recognit. 36 2 2003 563 566
    • (2003) Pattern Recognit. , vol.36 , Issue.2 , pp. 563-566
    • Yang, J.1    Yang, J.-Y.2
  • 30
    • 70350124939 scopus 로고    scopus 로고
    • Info-margin maximization for feature extraction
    • X. Qiu, and L. Wu Info-margin maximization for feature extraction Pattern Recognit. Lett. 30 16 2009 1516 1522
    • (2009) Pattern Recognit. Lett. , vol.30 , Issue.16 , pp. 1516-1522
    • Qiu, X.1    Wu, L.2
  • 31
    • 33144458972 scopus 로고    scopus 로고
    • Efficient and robust feature extraction by maximum margin criterion
    • H. Li, T. Jiang, and K. Zhang Efficient and robust feature extraction by maximum margin criterion IEEE Trans. Neural Netw. 17 1 2006 157 165
    • (2006) IEEE Trans. Neural Netw. , vol.17 , Issue.1 , pp. 157-165
    • Li, H.1    Jiang, T.2    Zhang, K.3
  • 33
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear component analysis as a kernel eigenvalue problem
    • B. Schölkopf, A. Smola, and K.-R. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10 5 1998 1299 1319
    • (1998) Neural Comput. , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Müller, K.-R.3
  • 35
  • 37
    • 84958672547 scopus 로고    scopus 로고
    • The Essex Face Database (accessed: 11.06.13)
    • The Essex Face Database (http://cswww.essex.ac.uk/mv/allfaces/) (accessed: 11.06.13).
  • 38
    • 84958665713 scopus 로고    scopus 로고
    • Fingerprint Verification Competition Database (accessed: 12.06.13)
    • Fingerprint Verification Competition Database (http://bias.csr.unibo.it/fvc2000/download.asp) (accessed: 12.06.13).
  • 39
    • 84958633265 scopus 로고    scopus 로고
    • Sheffield Face Database (accessed: 12.06.13)
    • Sheffield Face Database (http://www.sheffield.ac.uk/eee/research/iel/research/face) (accessed: 12.06.13).


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.