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Volumn 5, Issue 1, 2012, Pages 54-69

Composite distance metric integration by leveraging multiple experts' inputs and its application in patient similarity assessment

Author keywords

Distance metric integration; Multiple inputs; Patient similarity

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION SUPPORT SYSTEMS; QUADRATIC PROGRAMMING;

EID: 84863144899     PISSN: 19321872     EISSN: 19321864     Source Type: Journal    
DOI: 10.1002/sam.11135     Document Type: Article
Times cited : (26)

References (40)
  • 1
    • 85196057302 scopus 로고    scopus 로고
    • Rank-based distance metric learning: an application to image retrieval, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    • J.-E. Lee, R. Jin, and A. K. Jain, Rank-based distance metric learning: an application to image retrieval, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008, 1-8.
    • (2008) , pp. 1-8
    • Lee, J.-E.1    Jin, R.2    Jain, A.K.3
  • 2
    • 24644436425 scopus 로고    scopus 로고
    • Learning a similarity metric discriminatively, with application to face verification, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    • S. Chopra, R. Hadsell, and Y. LeCun, Learning a similarity metric discriminatively, with application to face verification, In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 1, 2005, 539-546.
    • (2005) , vol.1 , pp. 539-546
    • Chopra, S.1    Hadsell, R.2    LeCun, Y.3
  • 3
    • 33746131974 scopus 로고    scopus 로고
    • Kernel-based distance metric learning for microarray data classification
    • H. Xiong and X. Chen, Kernel-based distance metric learning for microarray data classification, BMC Bioinformatics 7 (2006), 299.
    • (2006) BMC Bioinformatics , vol.7 , pp. 299
    • Xiong, H.1    Chen, X.2
  • 4
    • 84964989291 scopus 로고    scopus 로고
    • Predicting patient's trajectory of physiological data using temporal trends in similar patients: A system for near-term prognostics, In AMIA Annual Symposium Proceedings
    • S. Ebadollahi, J. Sun, D. Gotz, J. Hu, D. Sow, and C. Neti, Predicting patient's trajectory of physiological data using temporal trends in similar patients: A system for near-term prognostics, In AMIA Annual Symposium Proceedings, 2010, 192-196.
    • (2010) , pp. 192-196
    • Ebadollahi, S.1    Sun, J.2    Gotz, D.3    Hu, J.4    Sow, D.5    Neti, C.6
  • 5
    • 78149476267 scopus 로고    scopus 로고
    • Localized supervised metric learning on temporal physiological data, In Proceedings of the International Conference on Pattern Recognition (ICPR)
    • J. Sun, D. Sow, J. Hu, and S. Ebadollahi, Localized supervised metric learning on temporal physiological data, In Proceedings of the International Conference on Pattern Recognition (ICPR), 2010, 4149-4152.
    • (2010) , pp. 4149-4152
    • Sun, J.1    Sow, D.2    Hu, J.3    Ebadollahi, S.4
  • 6
    • 85196054864 scopus 로고    scopus 로고
    • Distance metric learning: A comprehensive survey, Technical report, Department of Computer Science and Engineering, Michigan State University
    • L. Yang, Distance metric learning: A comprehensive survey, Technical report, Department of Computer Science and Engineering, Michigan State University, 2006.
    • (2006)
    • Yang, L.1
  • 10
    • 70349246930 scopus 로고    scopus 로고
    • Semi-supervised metric learning by maximizing constraint margin, In Proceedings of ACM 17th Conference on Information and Knowledge Management
    • F. Wang, S. Chen, T. Li, and C. Zhang, Semi-supervised metric learning by maximizing constraint margin, In Proceedings of ACM 17th Conference on Information and Knowledge Management, 2008, 1457-1458.
    • (2008) , pp. 1457-1458
    • Wang, F.1    Chen, S.2    Li, T.3    Zhang, C.4
  • 11
    • 85196069439 scopus 로고    scopus 로고
    • Distance metric learning with application to clustering with side-information, In Advances in Neural Information Processing System 15
    • E. Xing, A. Ng, M. Jordan, and S. Russell, Distance metric learning with application to clustering with side-information, In Advances in Neural Information Processing System 15, 2003, 505-512.
    • (2003) , pp. 505-512
    • Xing, E.1    Ng, A.2    Jordan, M.3    Russell, S.4
  • 13
    • 85196123116 scopus 로고    scopus 로고
    • Neighbourhood component analysis, In Advances in Neural Information Processing Systems 17
    • J. Goldberger, S. Roweis, G. Hinton, and R. Salakhutdinov, Neighbourhood component analysis, In Advances in Neural Information Processing Systems 17, 2005, 513-520.
    • (2005) , pp. 513-520
    • Goldberger, J.1    Roweis, S.2    Hinton, G.3    Salakhutdinov, R.4
  • 14
    • 61749090884 scopus 로고    scopus 로고
    • Distance metric learning for large margin nearest neighbor classification
    • K. Q. Weinberger and L. K. Saul, Distance metric learning for large margin nearest neighbor classification, J Mach Learn Res 10 (2009), 207-244.
    • (2009) J Mach Learn Res , vol.10 , pp. 207-244
    • Weinberger, K.Q.1    Saul, L.K.2
  • 15
    • 84887916087 scopus 로고
    • Regularized discriminant analysis
    • 405) ()
    • J. H. Friedman, Regularized discriminant analysis, J Am Stat Assoc 84(405) (1989), 165-175.
    • (1989) J Am Stat Assoc , vol.84 , pp. 165-175
    • Friedman, J.H.1
  • 16
    • 33745743918 scopus 로고    scopus 로고
    • Computational and theoretical analysis of null space and orthogonal linear discriminant analysis,
    • J. Ye and T. Xiong, Computational and theoretical analysis of null space and orthogonal linear discriminant analysis, Vol. 7, 2006, 1183-1204.
    • (2006) , vol.7 , pp. 1183-1204
    • Ye, J.1    Xiong, T.2
  • 18
    • 84880899766 scopus 로고    scopus 로고
    • Locality sensitive discriminant analysis, In Proceedings of the 20th International Joint Conference on Artificial Intelligence
    • D. Cai, X. He, K. Zhou, J. Han, and H. Bao, Locality sensitive discriminant analysis, In Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007. 708-713.
    • (2007) , pp. 708-713
    • Cai, D.1    He, X.2    Zhou, K.3    Han, J.4    Bao, H.5
  • 20
    • 85196120843 scopus 로고    scopus 로고
    • Multiple kernel learning, conic duality, and the smo algorithm, In Proceedings of International Conference on Machine Learning
    • F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, Multiple kernel learning, conic duality, and the smo algorithm, In Proceedings of International Conference on Machine Learning, 2004, 6-13.
    • (2004) , pp. 6-13
    • Bach, F.R.1    Lanckriet, G.R.G.2    Jordan, M.I.3
  • 23
    • 85196085068 scopus 로고    scopus 로고
    • Two-layer multiple kernel learning, In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Journal of Machine Learning Research W & CPs
    • J. Zhuang, I. W. Tsang, and S. C. Hoi, Two-layer multiple kernel learning, In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Journal of Machine Learning Research W & CPs, Vol. 15, 2011.
    • (2011) , vol.15
    • Zhuang, J.1    Tsang, I.W.2    Hoi, S.C.3
  • 24
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • 2) ()
    • L. Breiman, Bagging predictors, Mach Learn 24(2) (1996), 123-140.
    • (1996) Mach Learn , vol.24 , pp. 123-140
    • Breiman, L.1
  • 25
    • 84983110889 scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting, In European Conference on Computational Learning Theory
    • Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, In European Conference on Computational Learning Theory, 1995. 23-37.
    • (1995) , pp. 23-37
    • Freund, Y.1    Schapire, R.E.2
  • 28
    • 77951197218 scopus 로고    scopus 로고
    • Two heads better than one: metric+active learning and its applications for it service classification, In IEEE International Conference on Data Mining
    • F. Wang, J. Sun, T. Li, and N. Anerousis, Two heads better than one: metric+active learning and its applications for it service classification, In IEEE International Conference on Data Mining, 2009, 1022-1027.
    • (2009) , pp. 1022-1027
    • Wang, F.1    Sun, J.2    Li, T.3    Anerousis, N.4
  • 29
    • 85196063193 scopus 로고    scopus 로고
    • Spectral relaxation for k-means clustering, In Advances in Neural Information Processing Systems
    • H. Zha, X. He, C. Ding, H. Simon, and M. Gu, Spectral relaxation for k-means clustering, In Advances in Neural Information Processing Systems, 2001, 1057-1064.
    • (2001) , pp. 1057-1064
    • Zha, H.1    He, X.2    Ding, C.3    Simon, H.4    Gu, M.5
  • 31
    • 84942484786 scopus 로고
    • Ridge regression: biased estimation for nonorthogonal problems
    • A. E. Hoerl and R. Kennard, Ridge regression: biased estimation for nonorthogonal problems, Technometrics 12 (1970), 55-67.
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.2
  • 32
    • 56449092085 scopus 로고    scopus 로고
    • Efficient projections onto the l1-ball for learning in high dimensions, In Proceedings of the 25th international conference on Machine learning
    • J. Duchi, S. Shalev-Shwartz, Y. Singer, and T. Chandra, Efficient projections onto the l1-ball for learning in high dimensions, In Proceedings of the 25th international conference on Machine learning, 2008. 272-279.
    • (2008) , pp. 272-279
    • Duchi, J.1    Shalev-Shwartz, S.2    Singer, Y.3    Chandra, T.4
  • 33
    • 85196122339 scopus 로고    scopus 로고
    • Efficient euclidean projections in linear time, In Proceedings of the 26th international conference on Machine learning.
    • J. Liu and J. Ye, Efficient euclidean projections in linear time, In Proceedings of the 26th international conference on Machine learning.
    • Liu, J.1    Ye, J.2
  • 34
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • 1) ()
    • R. Tibshirani, Regression shrinkage and selection via the lasso, J R Stat Soc [Ser B] 58 (1) (1996), 267-288.
    • (1996) J R Stat Soc [Ser B] , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 36
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • 2) ()
    • H. Zou and T. Trevor, Regularization and variable selection via the elastic net, J R Stat Soc [Ser B] 67(2) (2005), 301-320.
    • (2005) J R Stat Soc [Ser B] , vol.67 , pp. 301-320
    • Zou, H.1    Trevor, T.2
  • 37
    • 51749101269 scopus 로고    scopus 로고
    • Approximate l0 constrained non-negative matrix and tensor factorization, In Proceedings of IEEE International Symposium on Circuits and Systems
    • M. Morup, K. H. Madsen, and L. K. Hansen, Approximate l0 constrained non-negative matrix and tensor factorization, In Proceedings of IEEE International Symposium on Circuits and Systems, 2008, 1328-1331.
    • (2008) , pp. 1328-1331
    • Morup, M.1    Madsen, K.H.2    Hansen, L.K.3
  • 38
    • 0004236492 scopus 로고    scopus 로고
    • 3rd ed.), Baltimore, MD, The Johns Hopkins University Press
    • G. H. Golub and C. F. V. Loan, Matrix Computation (3rd ed.), Baltimore, MD, The Johns Hopkins University Press, 1996.
    • (1996) Matrix Computation
    • Golub, G.H.1    Loan, C.F.V.2
  • 39
    • 33646023117 scopus 로고    scopus 로고
    • An introduction to roc analysis
    • T. Fawcett, An introduction to roc analysis, Pattern Recognit Lett 27 (2006), 861-874.
    • (2006) Pattern Recognit Lett , vol.27 , pp. 861-874
    • Fawcett, T.1
  • 40
    • 69549133517 scopus 로고    scopus 로고
    • Measuring classifier performance: a coherent alternative to the area under the roc curve
    • D. J. HMeasuring classifier performance: a coherent alternative to the area under the roc curve, Mach Learn 77 (2009), 103-123.
    • (2009) Mach Learn , vol.77 , pp. 103-123
    • Hand, D.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.