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Volumn , Issue , 2009, Pages 343-348

Human age estimation by metric learning for regression problems

Author keywords

[No Author keywords available]

Indexed keywords

DISTANCE METRIC LEARNING; FACE IMAGES; GAUSSIAN PROCESS REGRESSION; HUMAN AGE ESTIMATION; INTRINSIC AGING; LINEAR TRANSFORMATION; MANIFOLD LEARNING ALGORITHM; METRIC LEARNING; OPTIMIZATION PROBLEMS; PRIOR KNOWLEDGE; REAL-WORLD APPLICATION; REGRESSION METHOD; REGRESSION PROBLEM; TEST DATA;

EID: 70549108980     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CGIV.2009.91     Document Type: Conference Paper
Times cited : (7)

References (28)
  • 1
    • 70549103182 scopus 로고    scopus 로고
    • FG-NET Aging Database. http://www.fgnet.rsunit.com
  • 2
    • 35148892730 scopus 로고    scopus 로고
    • Biased manifold embedding: A framework for person-independent head pose estimation
    • V. N. Balasubramanian, J. Ye, and S. Panchanathan. Biased manifold embedding: A framework for person-independent head pose estimation. In IEEE Conf. CVPR, pp. 1-7, 2007.
    • (2007) IEEE Conf. CVPR , pp. 1-7
    • Balasubramanian, V.N.1    Ye, J.2    Panchanathan, S.3
  • 3
    • 78650291959 scopus 로고    scopus 로고
    • Learning distance function by coding similarity
    • A. Bar-Hillel and D. Weinshall. Learning distance function by coding similarity. Proc. ICML, pp. 65-72, 2007.
    • (2007) Proc. ICML , pp. 65-72
    • Bar-Hillel, A.1    Weinshall, D.2
  • 4
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for tearing from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: a geometric framework for tearing from labeled and unlabeled examples. Journal of Machine Learning Research, 7:2399-2434, 2006.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 9
    • 70349373021 scopus 로고    scopus 로고
    • When does geodesic distance recover the true hidden parametrization of families of articulated images?
    • D. L. Donoho and C. E. Grimes. When does geodesic distance recover the true hidden parametrization of families of articulated images? Proc. European Symposium on Artificial Neural Networks, 2002.
    • (2002) Proc. European Symposium on Artificial Neural Networks
    • Donoho, D.L.1    Grimes, C.E.2
  • 13
  • 16
    • 45949109037 scopus 로고    scopus 로고
    • Image-based human age estimation by manifold learning and locally adjusted robust regression
    • G. Guo, Y. Fu, C. Dyer, and T. S. Huang. Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. on Image Processing, 17:1178-1188, 2008.
    • (2008) IEEE Trans. on Image Processing , vol.17 , pp. 1178-1188
    • Guo, G.1    Fu, Y.2    Dyer, C.3    Huang, T.S.4
  • 19
    • 70349375765 scopus 로고    scopus 로고
    • Regression on manifolds using kernel dimension reduction
    • J. Nilsson, F. Sha, and M. I. Jordan. Regression on manifolds using kernel dimension reduction. IEEE Conf. ICML, pp. 265-272, 2007.
    • (2007) IEEE Conf. ICML , pp. 265-272
    • Nilsson, J.1    Sha, F.2    Jordan, M.I.3
  • 21
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • DOI 10.1126/science.290.5500.2323
    • S. T. Roweis, and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, 2000. (Pubitemid 32041578)
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 24
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionally reduction
    • J. B. Tenebaum, V. de. Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionally reduction. Science, 290(5500): 2319-2323, 2000.
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenebaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 25
    • 33749550361 scopus 로고    scopus 로고
    • Distance metric learning for large margin nearest neighbor classification
    • K.Weinberger, J. Blitzer, and L. Saul. Distance metric learning for large margin nearest neighbor classification. Proc. NIPS, pp. 1475-1482, 2006.
    • (2006) Proc. NIPS , pp. 1475-1482
    • Weinberger, K.1    Blitzer, J.2    Saul, L.3
  • 26
    • 85133386144 scopus 로고    scopus 로고
    • Distance metric learning with application to clustering with side-information
    • E. Xing, A. Ng, M. I. Jordan, and S. Russell. Distance metric learning with application to clustering with side-information. Proc. NIPS, 2002.
    • (2002) Proc. NIPS
    • Xing, E.1    Ng, A.2    Jordan, M.I.3    Russell, S.4
  • 28
    • 50649098885 scopus 로고    scopus 로고
    • Learning autostructured regressor from uncertain nonnegative labels
    • S. Yan, H. Wang, X. Tang, and T. S. Huang. Learning autostructured regressor from uncertain nonnegative labels. IEEE Conf. ICCV, pp. 1-8, 2007.
    • (2007) IEEE Conf. ICCV , pp. 1-8
    • Yan, S.1    Wang, H.2    Tang, X.3    Huang, T.S.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.