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Volumn , Issue , 2004, Pages 495-498

A comparative study of linear and nonlinear feature extraction methods

Author keywords

[No Author keywords available]

Indexed keywords

DATASETS; LINEAR DISCRIMINANT ANALYSIS (LDA); NEAREST-NEIGHBOR CLASSIFIERS; NONLINEAR DISCRIMINANT ANALYSIS;

EID: 19544371340     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (15)

References (8)
  • 1
    • 0034300875 scopus 로고    scopus 로고
    • A new LDA-based face recognition system which can solve the small sample size problem
    • L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu. A new LDA-based face recognition system which can solve the small sample size problem, pattern recognition, 33:1713-1726, 2000.
    • (2000) Pattern Recognition , vol.33 , pp. 1713-1726
    • Chen, L.1    Liao, H.2    Ko, M.3    Lin, J.4    Yu, G.5
  • 5
    • 1342310014 scopus 로고    scopus 로고
    • Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition
    • P. Howland, M. Jeon, and H. Park. Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 25(1):165-179, 2003.
    • (2003) SIAM Journal on Matrix Analysis and Applications , vol.25 , Issue.1 , pp. 165-179
    • Howland, P.1    Jeon, M.2    Park, H.3
  • 6
    • 19544384107 scopus 로고    scopus 로고
    • A comparative study of linear and nonlinear feature extraction methods
    • Department of Computer Science and Engineering, University of Minnesosta, Twin Cities
    • C.H. Park, H. Park and P. Pardalos. A comparative study of linear and nonlinear feature extraction methods. Technical Reports 04-032, Department of Computer Science and Engineering, University of Minnesosta, Twin Cities, 2004.
    • (2004) Technical Reports , vol.4 , Issue.32
    • Park, C.H.1    Park, H.2    Pardalos, P.3
  • 7
    • 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 Recognition, 36:563-566, 2003.
    • (2003) Pattern Recognition , vol.36 , pp. 563-566
    • Yang, J.1    Yang, J.-Y.2
  • 8
    • 0001765951 scopus 로고    scopus 로고
    • A direct LDA algorithm for high-dimensional data- with application to face recognition
    • H. Yu and J. Yang. A direct LDA algorithm for high-dimensional data- with application to face recognition. Pattern Recognition, 34:2067-2070, 2001.
    • (2001) Pattern Recognition , vol.34 , pp. 2067-2070
    • Yu, H.1    Yang, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.