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Volumn 19, Issue 11, 2008, Pages 2822-2832

Semi-supervised canonical correlation analysis algorithm

Author keywords

Canonical correlation analysis; Classification; Dimensionality reduction; Pair wise constraints; Semi supervised learning

Indexed keywords

CORRELATION METHODS; MACHINE LEARNING; SUPERVISED LEARNING;

EID: 56549112814     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/sp.j.1001.2008.02822     Document Type: Article
Times cited : (33)

References (28)
  • 3
    • 33144473787 scopus 로고    scopus 로고
    • Facial expression recognition using kernel canonical correlation analysis (KCCA)
    • Zheng W M, Zhou X Y, Zou C R, Zhao L. Facial expression recognition using kernel canonical correlation analysis (KCCA). IEEE Trans. on Neural Networks, 2006, 17(1):233-238.
    • (2006) IEEE Trans. on Neural Networks , vol.17 , Issue.1 , pp. 233-238
    • Zheng, W.M.1    Zhou, X.Y.2    Zou, C.R.3    Zhao, L.4
  • 5
    • 33745814209 scopus 로고    scopus 로고
    • The canonical correlations of color images and their use for demosaicing
    • HP Labs.
    • Hel-Or Y. The canonical correlations of color images and their use for demosaicing. Technical Report, HPL-2003-164(R1), HP Labs., 2004.
    • (2004) Technical Report, HPL-2003-164(R1)
    • Hel-Or, Y.1
  • 8
    • 0036505017 scopus 로고    scopus 로고
    • Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
    • Nielsen A A. Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data. IEEE Trans. on Image Processing, 2002, 11(3):293-305.
    • (2002) IEEE Trans. on Image Processing , vol.11 , Issue.3 , pp. 293-305
    • Nielsen, A.A.1
  • 10
    • 10144261309 scopus 로고    scopus 로고
    • Dimensionality reductions approach to multivariate prediction
    • Abraham B, Merola G. Dimensionality reductions approach to multivariate prediction. Computational Statistics and Data Analysis, 2005, 48(1):5-16.
    • (2005) Computational Statistics and Data Analysis , vol.48 , Issue.1 , pp. 5-16
    • Abraham, B.1    Merola, G.2
  • 11
    • 33748530659 scopus 로고    scopus 로고
    • Using KCCA for Japanese-English cross-language information retrieval and document classification
    • Li Y Y, Shawe-Taylor J. Using KCCA for Japanese-English cross-language information retrieval and document classification. Journal of Intelligent Information Systems, 2006, 27(2):117-133.
    • (2006) Journal of Intelligent Information Systems , vol.27 , Issue.2 , pp. 117-133
    • Li, Y.Y.1    Shawe-Taylor, J.2
  • 14
    • 33745456231 scopus 로고    scopus 로고
    • Semi-supervised learning literature survey
    • Madison: Department of Computer Sciences, University of Wisconsin
    • Zhu X J. Semi-Supervised learning literature survey. Technical Report, 1530, Madison: Department of Computer Sciences, University of Wisconsin, 2005.
    • (2005) Technical Report, 1530
    • Zhu, X.J.1
  • 17
    • 48349091260 scopus 로고    scopus 로고
    • Large margin vs. large volume in transductive learning
    • El-Yaniv R, Pechyony D, Vapnik V. Large margin vs. large volume in transductive learning. Machine Learning, 2008, 72(3): 173-188.
    • (2008) Machine Learning , vol.72 , Issue.3 , pp. 173-188
    • El-Yaniv, R.1    Pechyony, D.2    Vapnik, V.3
  • 22
    • 84864072823 scopus 로고    scopus 로고
    • Analysis of spectral kernel design based semi-supervised learning
    • Cambridge: MIT Press
    • Zhang T, Ando R K. Analysis of spectral kernel design based semi-supervised learning. In: Neural Information Processing Systems. Cambridge: MIT Press, 2006. 1601-1608.
    • (2006) Neural Information Processing Systems , pp. 1601-1608
    • Zhang, T.1    Ando, R.K.2
  • 26
    • 25144439113 scopus 로고    scopus 로고
    • A new method of feature fusion and its application in image recognition
    • Sun Q S, Zeng S G, Liu Y, Heng P A, Xia D S. A new method of feature fusion and its application in image recognition. Pattern Recognition, 2005, 38(12):2437-2448.
    • (2005) Pattern Recognition , vol.38 , Issue.12 , pp. 2437-2448
    • Sun, Q.S.1    Zeng, S.G.2    Liu, Y.3    Heng, P.A.4    Xia, D.S.5
  • 27
    • 33845646938 scopus 로고    scopus 로고
    • A learning algorithm for adaptive canonical correlation analysis of several data sets
    • Vía J, Santamaría I, Pérez J. A learning algorithm for adaptive canonical correlation analysis of several data sets. Neural Networks, 2007, 20(1):139-152.
    • (2007) Neural Networks , vol.20 , Issue.1 , pp. 139-152
    • Vía, J.1    Santamaría, I.2    Pérez, J.3
  • 28
    • 0038648412 scopus 로고    scopus 로고
    • Appearance models based on kernel canonical correlation analysis
    • Melzer T, Reiter M, Bischof H. Appearance models based on kernel canonical correlation analysis. Pattern Recognition, 2003, 36(9):1961-1971.
    • (2003) Pattern Recognition , vol.36 , Issue.9 , pp. 1961-1971
    • Melzer, T.1    Reiter, M.2    Bischof, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.