메뉴 건너뛰기




Volumn 74, Issue 12-13, 2011, Pages 2201-2211

Learning low-rank kernel matrices for constrained clustering

Author keywords

Constrained clustering; Distance metric; Kernel learning; Low rank kernel; Semi supervised; Spectral

Indexed keywords

CONSTRAINED CLUSTERING; DISTANCE METRIC; KERNEL LEARNING; LOW-RANK KERNEL; SEMI-SUPERVISED; SPECTRAL;

EID: 79956106285     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.02.009     Document Type: Article
Times cited : (16)

References (52)
  • 2
    • 12244300524 scopus 로고    scopus 로고
    • A probabilistic framework for semi-supervised clustering
    • Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
    • S. Basu, M. Bilenko, R.J. Mooney, A probabilistic framework for semi-supervised clustering, in: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 59-68.
    • (2004) , pp. 59-68
    • Basu, S.1    Bilenko, M.2    Mooney, R.J.3
  • 3
    • 79956154486 scopus 로고    scopus 로고
    • Semi-supervised clustering: probabilistic models, algorithms and experiments, Ph.D. Dissertation, University of Texas at Austin.
    • S. Basu, Semi-supervised clustering: probabilistic models, algorithms and experiments, Ph.D. Dissertation, University of Texas at Austin, 2005.
    • (2005)
    • Basu, S.1
  • 4
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 2003, 15(6):1373-1396.
    • (2003) Neural Computation , vol.15 , Issue.6 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 6
    • 49049111209 scopus 로고    scopus 로고
    • Semi-supervised discriminant analysis, in: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV).
    • D. Cai, H. Xiaofei, H. Jiawei, Semi-supervised discriminant analysis, in: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV), 2007, pp. 1-7.
    • (2007) , pp. 1-7
    • Cai, D.1    Xiaofei, H.2    Jiawei, H.3
  • 8
    • 33646084850 scopus 로고    scopus 로고
    • Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
    • Chang H., Yeung D.Y. Locally linear metric adaptation with application to semi-supervised clustering and image retrieval. Pattern Recognition 2006, 39:1253-1264.
    • (2006) Pattern Recognition , vol.39 , pp. 1253-1264
    • Chang, H.1    Yeung, D.Y.2
  • 9
    • 84898956003 scopus 로고    scopus 로고
    • Kernel design using boosting, in: Proceedings of Advances in Neural Information Processing Systems, MIT Press.
    • K. Crammer, J. Keshet, Y. Singer, Kernel design using boosting, in: Proceedings of Advances in Neural Information Processing Systems, vol. 15, MIT Press, 2003, pp. 537-544.
    • (2003) , vol.15 , pp. 537-544
    • Crammer, K.1    Keshet, J.2    Singer, Y.3
  • 10
    • 84898936871 scopus 로고    scopus 로고
    • On kernel target alignment, in: Proceedings of Advances in Neural Information Processing Systems
    • MIT Press
    • N. Cristianini, J. Kandola, A. Elisseeff, J. Shawe-Taylor, On kernel target alignment, in: Proceedings of Advances in Neural Information Processing Systems, vol. 14, MIT Press, 2002, pp. 367-373.
    • (2002) , vol.14 , pp. 367-373
    • Cristianini, N.1    Kandola, J.2    Elisseeff, A.3    Shawe-Taylor, J.4
  • 15
    • 79956140392 scopus 로고
    • Flexible metric nearest neighbor classification, Technical Report, Statistics Department, Stanford University.
    • J.H. Friedman, Flexible metric nearest neighbor classification, Technical Report, Statistics Department, Stanford University, 1994.
    • (1994)
    • Friedman, J.H.1
  • 21
    • 44449138321 scopus 로고    scopus 로고
    • Toward effective document clustering: a constrained k-means based approach
    • Hu G., Zhou S., Guan J., Hu X. Toward effective document clustering: a constrained k-means based approach. Information processing and management 2008, 44:1397-1409.
    • (2008) Information processing and management , vol.44 , pp. 1397-1409
    • Hu, G.1    Zhou, S.2    Guan, J.3    Hu, X.4
  • 24
    • 79956152238 scopus 로고    scopus 로고
    • CLUTO-a clustering toolkit, Technical Report 02-017, Department of Computer Science, University of Minnesota.
    • G. Karypis, CLUTO-a clustering toolkit, Technical Report 02-017, Department of Computer Science, University of Minnesota, 2002.
    • (2002)
    • Karypis, G.1
  • 25
    • 9444294778 scopus 로고    scopus 로고
    • From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering
    • Sydney, Australia
    • D. Klein, S.D. Kamvar, C. Manning, From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering, in: Proceedings of the 19th International Conference on Machine Learning (ICML), Sydney, Australia, 2002, pp. 307-314.
    • (2002) Proceedings of the 19th International Conference on Machine Learning (ICML) , pp. 307-314
    • Klein, D.1    Kamvar, S.D.2    Manning, C.3
  • 27
    • 58149202361 scopus 로고    scopus 로고
    • Semi-supervised graph clustering: a kernel approach
    • Kulis B., Basu S., Dhillon I. Semi-supervised graph clustering: a kernel approach. Machine Learning 2009, 74(1):1-22.
    • (2009) Machine Learning , vol.74 , Issue.1 , pp. 1-22
    • Kulis, B.1    Basu, S.2    Dhillon, I.3
  • 30
    • 79956155603 scopus 로고    scopus 로고
    • Clustering, dimensionality reduction, and side information, Ph.D. Dissertation, Michigan University.
    • M.H.C. Law, Clustering, dimensionality reduction, and side information, Ph.D. Dissertation, Michigan University, 2006.
    • (2006)
    • Law, M.H.C.1
  • 31
    • 79956149848 scopus 로고    scopus 로고
    • Numerical methods for sparse nonlinear eigenvalue problem, Technical Report, Department of Mathematics, Hamburg University of Technology.
    • H. Voss, Numerical methods for sparse nonlinear eigenvalue problem, Technical Report, Department of Mathematics, Hamburg University of Technology, 2003.
    • (2003)
    • Voss, H.1
  • 35
    • 70450267700 scopus 로고    scopus 로고
    • Constrained clustering via spectral regularization, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)
    • Z. Li, J. Liu, X. Tang, Constrained clustering via spectral regularization, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 421-428.
    • (2009) , pp. 421-428
    • Li, Z.1    Liu, J.2    Tang, X.3
  • 36
    • 0001920729 scopus 로고
    • Similarity metric learning for a variable-kernel classifier
    • Lowe D.G. Similarity metric learning for a variable-kernel classifier. Neural Computation 1995, 7(1):72-85.
    • (1995) Neural Computation , vol.7 , Issue.1 , pp. 72-85
    • Lowe, D.G.1
  • 38
    • 0019610270 scopus 로고
    • The optimal distance measure for nearest neighbor classification
    • Short R.D., Fukunaga K. The optimal distance measure for nearest neighbor classification. IEEE Transactions on Information Theory 1981, 27(5):622-627.
    • (1981) IEEE Transactions on Information Theory , vol.27 , Issue.5 , pp. 622-627
    • Short, R.D.1    Fukunaga, K.2
  • 40
    • 77649151199 scopus 로고    scopus 로고
    • Metric learning for semi-supervised clustering using pairwise constraints and the geometrical structure of data
    • Soleymani Baghshah M., Bagheri Shouraki S. Metric learning for semi-supervised clustering using pairwise constraints and the geometrical structure of data. Intelligent Data Analysis 2009, 13(6):887-899.
    • (2009) Intelligent Data Analysis , vol.13 , Issue.6 , pp. 887-899
    • Soleymani Baghshah, M.1    Bagheri Shouraki, S.2
  • 41
    • 77949267074 scopus 로고    scopus 로고
    • Kernel-based metric learning for semi-supervised clustering
    • Soleymani Baghshah M., Bagheri Shouraki S. Kernel-based metric learning for semi-supervised clustering. Neurocomputing 2010, 73:1352-1361.
    • (2010) Neurocomputing , vol.73 , pp. 1352-1361
    • Soleymani Baghshah, M.1    Bagheri Shouraki, S.2
  • 42
    • 79956114640 scopus 로고    scopus 로고
    • Metric learning with convex optimization, Ph.D. Dissertation, University of Pennsylvania.
    • K.Q. Weinberger, Metric learning with convex optimization, Ph.D. Dissertation, University of Pennsylvania, 2007.
    • (2007)
    • Weinberger, K.Q.1
  • 43
    • 84863346514 scopus 로고    scopus 로고
    • Learning Bregman distance functions and its application for semi-supervised clustering
    • MIT Press, Cambridge, MA, USA
    • Wu L., Jin R., Hoi S.C.H., Zhu J., Yu N. Learning Bregman distance functions and its application for semi-supervised clustering. Advances in Neural Information Processing Systems 2009, MIT Press, Cambridge, MA, USA.
    • (2009) Advances in Neural Information Processing Systems
    • Wu, L.1    Jin, R.2    Hoi, S.C.H.3    Zhu, J.4    Yu, N.5
  • 44
    • 49449088902 scopus 로고    scopus 로고
    • Learning a Mahalanobis distance metric for data clustering and classification
    • Xiang S., Nie F., Zhang C. Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition 2008, 41(12):3600-3612.
    • (2008) Pattern Recognition , vol.41 , Issue.12 , pp. 3600-3612
    • Xiang, S.1    Nie, F.2    Zhang, C.3
  • 45
    • 84879571292 scopus 로고    scopus 로고
    • Distance metric learning with application to clustering with side information
    • Proceedings of Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, USA
    • E.P. Xing, A.Y. Ng, M.I. Jordan, S. Russell, Distance metric learning with application to clustering with side information, in: Proceedings of Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, USA, 2003, pp. 505-512.
    • (2003) , vol.15 , pp. 505-512
    • Xing, E.P.1    Ng, A.Y.2    Jordan, M.I.3    Russell, S.4
  • 46
    • 79956097692 scopus 로고    scopus 로고
    • Distance metric learning: a comprehensive survey, Technical Report, Michigan State University.
    • L. Yang, R. Jin, Distance metric learning: a comprehensive survey, Technical Report, Michigan State University, 2006.
    • (2006)
    • Yang, L.1    Jin, R.2
  • 47
    • 33244489358 scopus 로고    scopus 로고
    • Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints
    • Yeung D.Y., Chang H. Extending the relevant component analysis algorithm for metric learning using both positive and negative equivalence constraints. Pattern Recognition 2006, 39:1007-1010.
    • (2006) Pattern Recognition , vol.39 , pp. 1007-1010
    • Yeung, D.Y.1    Chang, H.2
  • 48
    • 33846040203 scopus 로고    scopus 로고
    • A Kernel approach for semi-supervised metric learning
    • Yeung D.Y., Chang H. A Kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks 2007, 18(1):141-149.
    • (2007) IEEE Transactions on Neural Networks , vol.18 , Issue.1 , pp. 141-149
    • Yeung, D.Y.1    Chang, H.2
  • 49
    • 55749089518 scopus 로고    scopus 로고
    • A scalable kernel-based semi-supervised metric learning algorithm with out-of-sample generation ability
    • Yeung D.Y., Chang H., Dai G. A scalable kernel-based semi-supervised metric learning algorithm with out-of-sample generation ability. Neural Computation 2008, 20(11):2839-2861.
    • (2008) Neural Computation , vol.20 , Issue.11 , pp. 2839-2861
    • Yeung, D.Y.1    Chang, H.2    Dai, G.3
  • 50
    • 79956101120 scopus 로고    scopus 로고
    • Kernel optimization using pairwise constraints for semi-supervised clustering
    • MIT Press, Cambridge, MA, USA
    • Yan B., Domeniconi C. Kernel optimization using pairwise constraints for semi-supervised clustering. Advances in Neural Information Processing Systems 2009, MIT Press, Cambridge, MA, USA.
    • (2009) Advances in Neural Information Processing Systems
    • Yan, B.1    Domeniconi, C.2
  • 51
    • 74449083176 scopus 로고    scopus 로고
    • Semi-supervised clustering with metric learning: an adaptive kernel method
    • Yin X., Chen S., Hu E., Zhang D. Semi-supervised clustering with metric learning: an adaptive kernel method. Pattern Recognition 2010, 43:1320-1333.
    • (2010) Pattern Recognition , vol.43 , pp. 1320-1333
    • Yin, X.1    Chen, S.2    Hu, E.3    Zhang, D.4
  • 52
    • 70049084031 scopus 로고    scopus 로고
    • Simple NPKL: simple non-parametric kernel learning, Proceedings of the 26th International Conference on Machine Learning (ICML), Montreal, Canada.
    • J. Zhuang, I.W. Tsang, S.C.H. Hoi, Simple NPKL: simple non-parametric kernel learning, in: Proceedings of the 26th International Conference on Machine Learning (ICML), Montreal, Canada, 2009.
    • (2009)
    • Zhuang, J.1    Tsang, I.W.2    Hoi, S.C.H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.