Constraint projections for semi-supervised affinity propagation

Knowledge-Based Systems

Volumn 36, Issue , 2012, Pages 315-321

  Wang, Hongjun ^a   Nie, Ruihua ^b   Liu, Xingnian ^b   Li, Tianrui ^a  

^a SOUTHWEST JIAOTONG UNIVERSITY   (China)

^b SICHUAN UNIVERSITY   (China)

Author keywords

Affinity propagation; Constraint projections; Semi supervised affinity propagation

Indexed keywords

AFFINITY PROPAGATION; CONSTRAINT PROJECTIONS; DATA POINTS; DATA SETS; DIMENSIONAL SPACES; SEMI-SUPERVISED; SEMI-SUPERVISED CLUSTERING; UCI MACHINE LEARNING REPOSITORY;

CLUSTERING ALGORITHMS;

LEARNING ALGORITHMS;

EID: 84867858093     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2012.05.011     Document Type: Article

References (19)

1. C. Bryan, T.X. Yongxin, Semi-Supervised Clustering Using Affinity Propagation, Technology Report, 2009. < http://www.princeton.edu/yxi/project/ ssap.pdf >.
2. E.G. Inmar, J.F. Brendan, Semi-supervised affinity propagation with instance-level constraints, in: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, 2009.
3. K. Wagstaff, C. Cardie, S. Rogers, S. Schroedl, Constrained k-means clustering with background knowledge, in: ICML, 2001.
4. B.J. Frey, and D. Dueck Clustering by passing messages between data points Science 305 5814 2007 972 976
5. E.P. Xing, A.Y. Ng, M.I. Jordan, S. Russell, Distance metric learning, with application to clustering with side-information, in: NIPS, 2003.
6. Marc Mzard Where are the exemplars? Science 315 2007 949 951
7. Ian Davidson, and Sugato Basu A survey of clustering with instance level constraints ACM Transactions on Knowledge Discovery from Data w x, z 2007 1 41
8. D. Zhang, S. Chen, Z.-H. Zhou, Q. Yang, Constraint projections for ensemble learning, in: Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'08), Chicago, IL, 2008, pp. 758-763.
9. J. Tenenbaum, V. Silva, and J. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 2000 2319 2323
10. Z. Zhou, and W. Tang Clusterer ensemble Knowledge-Based Systems 1 2006 77 83
11. Sugato Basu, Mikhail Bilenko, Raymond J. Mooney, A probabilistic framework for semi-supervised clustering, in: KDD 04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2004, pp. 59-68.
12. D. Zhou, O. Bousquet, T.N. Lal, et al., Learning with local and global consistency, in: Proc. NIPS, Vancouver, BC, Canada, 2004.
13. Jie Gui, D.S. Huang, Zhuhong You, An improvement on learning with local and global consistency, in: 2008 The 19th International Conference on Pattern Recognition (ICPRI2008), Tampa, Florida, USA, December 8-11, 2008, pp. 1-4.
14. F. Wang, and C.S. Zhang Label propagation through linear neighborhoods IEEE Transactions on Knowledge and Data Engineering 20 2008 55 67
15. Deng Cai, Xiaofei He, and Jiawei Han Document clustering using locality preserving indexing IEEE Transactions on Knowledge and Data Engineering 17 12 2005
16. Xiaofei He, and Partha Niyogi Locality Preserving Projections NIPS 16 2003
17. J. Demsar Statistical comparisons of classifiers over multiple data sets Journal Of Machine Learning Research 7 2006 1 30
18. Salvador Garcła, Alberto Fernndez, J. Luengo, and F. Herrera Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power Information Sciences 180 2010 2044 2064
19. WenYen Chen, Yangqiu Song, Hongjie Bai, Chih-Jen Lin, Edward Y. Chang, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 33(3), March 2011, pp. 568-586.